{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-RAG\n",
    "\n",
    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval\n",
    "or generations.\n",
    "\n",
    "Several recent papers focus on this theme, but implementing the ideas can be\n",
    "tricky.\n",
    "\n",
    "Here we show how to implement ideas from the `Self RAG` paper\n",
    "[here](https://arxiv.org/abs/2310.11511) using LangGraph.\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "Set `OPENAI_API_KEY`\n",
    "\n",
    "## Self-RAG Detail\n",
    "\n",
    "Self-RAG is a recent paper that introduces an interesting approach for\n",
    "self-reflective RAG.\n",
    "\n",
    "The framework trains an LLM (e.g., LLaMA2-7b or 13b) to generate tokens that\n",
    "govern the RAG process in a few ways:\n",
    "\n",
    "1. Should I retrieve from retriever, `R` -\n",
    "\n",
    "- Token: `Retrieve`\n",
    "- Input: `x (question)` OR `x (question)`, `y (generation)`\n",
    "- Decides when to retrieve `D` chunks with `R`\n",
    "- Output: `yes, no, continue`\n",
    "\n",
    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
    "\n",
    "- Token: `ISREL`\n",
    "-\n",
    "  - Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
    "- `d` provides useful information to solve `x`\n",
    "- Output: `relevant, irrelevant`\n",
    "\n",
    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk\n",
    "   (hallucinations, etc) -\n",
    "\n",
    "- Token: `ISSUP`\n",
    "- Input: `x (question)`, `d (chunk)`, `y (generation)` for `d` in `D`\n",
    "- All of the verification-worthy statements in `y (generation)` are supported by\n",
    "  `d`\n",
    "- Output: `{fully supported, partially supported, no support`\n",
    "\n",
    "4. The LLM generation from each chunk in `D` is a useful response to\n",
    "   `x (question)` -\n",
    "\n",
    "- Token: `ISUSE`\n",
    "- Input: `x (question)`, `y (generation)` for `d` in `D`\n",
    "- `y (generation)` is a useful response to `x (question)`.\n",
    "- Output: `{5, 4, 3, 2, 1}`\n",
    "\n",
    "We can represent this as a graph:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "---\n",
    "\n",
    "Let's implement some of these ideas from scratch using\n",
    "[LangGraph](https://js.langchain.com/docs/langgraph)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "### Load env vars\n",
    "\n",
    "Add a `.env` variable in the root of the repo folder with your variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import \"dotenv/config\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install dependencies\n",
    "\n",
    "```bash\n",
    "npm install cheerio zod langchain @langchain/community @langchain/openai @langchain/core @langchain/textsplitters @langchain/langgraph\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { CheerioWebBaseLoader } from \"@langchain/community/document_loaders/web/cheerio\";\n",
    "import { RecursiveCharacterTextSplitter } from \"@langchain/textsplitters\";\n",
    "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
    "import { OpenAIEmbeddings } from \"@langchain/openai\";\n",
    "\n",
    "const urls = [\n",
    "  \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
    "  \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
    "  \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
    "];\n",
    "\n",
    "const docs = await Promise.all(\n",
    "  urls.map((url) => new CheerioWebBaseLoader(url).load()),\n",
    ");\n",
    "const docsList = docs.flat();\n",
    "\n",
    "const textSplitter = new RecursiveCharacterTextSplitter({\n",
    "  chunkSize: 500,\n",
    "  chunkOverlap: 250,\n",
    "});\n",
    "const docSplits = await textSplitter.splitDocuments(docsList);\n",
    "\n",
    "// Add to vectorDB\n",
    "const vectorStore = await MemoryVectorStore.fromDocuments(\n",
    "  docSplits,\n",
    "  new OpenAIEmbeddings({ model: \"text-embedding-3-large\" }),\n",
    ");\n",
    "const retriever = vectorStore.asRetriever();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State\n",
    "\n",
    "We will define a graph.\n",
    "\n",
    "Our state will be an `object`.\n",
    "\n",
    "We can access this from any graph node as `state.key`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { Annotation } from \"@langchain/langgraph\";\n",
    "import { type DocumentInterface } from \"@langchain/core/documents\";\n",
    "\n",
    "// Represents the state of our graph.\n",
    "const GraphState = Annotation.Root({\n",
    "  documents: Annotation<DocumentInterface[]>({\n",
    "    reducer: (x, y) => y ?? x ?? [],\n",
    "  }),\n",
    "  question: Annotation<string>({\n",
    "    reducer: (x, y) => y ?? x ?? \"\",\n",
    "  }),\n",
    "  generation: Annotation<string>({\n",
    "    reducer: (x, y) => y ?? x,\n",
    "    default: () => \"\",\n",
    "  }),\n",
    "  generationVQuestionGrade: Annotation<string>({\n",
    "    reducer: (x, y) => y ?? x,\n",
    "  }),\n",
    "  generationVDocumentsGrade: Annotation<string>({\n",
    "    reducer: (x, y) => y ?? x,\n",
    "  }),\n",
    "});"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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96927N0UbCwq/SYAESIAESIAESIAESIAESMCLCFC0qcRmpKSkyMUXXywHDx6070pNTZW//vpLJkyYIF988YWcddZZ9rUjNcLCwmTs2LGm26JFi+Sdd9450i3mOjxsLMGmS5cuctNNN0mDBg0kIiKiQve7dlq+fLns3bvX9ZRpQ5j55ptvZMqUKcaLx7XDDz/8ICtWrDCnIDx5Em3mzZsnEITAxzK0Id7g/s8//1wgMlXU0tPTzTNzc3MressJ7ffBBx/Iiy++6DYHMMPn559/Np/Y2Fi36zwgARIgARIgARIgARIgARIgARIgAVcCFG1caRyh/d133xnBJjg4WMaNG2c8bLZu2ypPP/W0ESfgRVIZgxdLnz59zC1ZWVkVvnX8+PGmb6dOnYyoUuEby+n4448/SsOGDSUtLc0ICm+//basW7dOxowZI3feead9J0ST3377TcAAHjkQYeBRA9HIMgg5lmCDOf73v/+Vtm3bmn6PP/647N+/X8444wyre637hqhmCTbXXnutPProo8YLCzzvuusu6dmzp2FX6xbOBZEACZAACZAACZAACZAACZAACVQpAYo2lcC5detW0/uyyy6T008/3bRbt2ptPFIgZgQGBrqNhrAjeN+sX79e/Pz8TGjMbbfdJuHh4W79Knuwbds2c4sl+FT2fk/9GzdubAsJEJ8QjgRvG4T4uNoff/xhDvv27SvwFAETiDjXXXed3Q15euBVA1EHYhDy7cAQPvbJ6E8kLzfPiD72DdXQuO+++6SwsNB4HzVr1swIJdaeuT4OglJycrIJWYK3FNYXGhoqCPu64IILXLvKoUOHTA6i1atXmz7XXHONWdu0adPkwgsvFIQZwSzB5tJLL5XXX3/dHgNckUMGgpePj4/88ssvAo8kGAS85557TmbNmmWYZWdnG68uPMMyhOVBJENfrOmqq66SFi1aWJftb2tNjzzyiEDgW7hwodStW1f69e8nl116md3PtYE+8KrCvmOe4BcZGenahW0SIAESIAESIAESIAESIAESIIHjTICiTSWAwzvkq6++Mj+08eMWP5xh+AFeUrBBuNPNN98srh40+MH+9ddfy9SpU6VevXqVeLJ7Vys8C/lvqsvgGQPLz893ewREBVifvn2kUUwjk9sG51xFm/nz55s+d999ty3YmBP6x8/hJ37B1fvaQVyB4FTSIMQgrAz7ZRm8pxAehtA313sgYEAkgTACgwcSxDpLuMM59EECX4Q7wdMIok1+Qb4RfnAduWtKWkhIiH1q6dKlRtSzTnTv3t144ljHeF/Q/5JLLjGnfvrpJyPaWNf/97//Cd4BeENBlLHMWlNGRoYJRbPOIyxt3dp18thjj1mnzDfWPmzYMPtdhZCEZ8+cOdNjTiO3m3lAAiRAAiRAAiRAAiRAAiRAAiRQbQRY8rsSaOFNAS8J/Mg/99xz5fnnnxckJi5p8PB48sknzY/gHj16CPKbvPnmm9KmTRtzL35sV9YmTZpkxsFYCEeCQfzBsfVBWFJVGPLHTJw40QwFIcHVfv31V3PYu1dvO3/PnDlzJC8vz+62c+dO00bS3RNhQUFBAl7wMoFIg4TKMKwJgoYnw7oefvhhkzgYewb78MMP7a6fffaZEWzq1KljxoVXzuWXX24EG7uTNvbtPfw+nHLKKa6XSrXhzQLByxL/4CHzxBNPmHO9evUy/SEMWQYRCWtCEmiEn8GTCTlyXnjhBauL2zfej6eeesp4QsHrBoa8SSVzGMFzCJ5TWJOVKwni1OzZs93G4wEJkAAJkAAJkAAJkAAJkAAJkMDxJVC9Lg/Hdy3V/jSENcH7AD+2V65caX484wf0yJEj5ZZbbrGfj3Cov//+2xzjOn7ow+CNAQHh999/N8eV+YPEvfDMcDUrt411DiKCqyeHdb4i3//85z9NyA88SqyQHQhU/fr1s29HafHdu3cLypKjahYMAgcqaSGhsZWE2RIFrDw3CQkJxkPJHkgbyPVSXYl4ET7k6oWEEurYO4hbCxYskCFDhrhOxbThKfSPf/zDtCH6DB48WKwwNJyElwsM4W3WOpEEGmKPqzdV/J540w+eVL6+Tk0UIWYQtlwN1caioqLMB5xhCFG79957TRsePtgHhG5Z1qFDB6tpQrcwD7x3Jce2OmEN8HaC4Xnw8sLeTJ8+3XjWWP3wDcEIiaxRNQwJqBEm5rp+175skwAJkAAJkAAJkAAJkAAJkAAJHB8CFG0qyblp06bGIwFeCe+9957xvnj66adl2bJlguS9MIROwfDD/ZNPPjFt/LG8Uazr9oUKNJ555hn7Bzy8JyCewDMEwoFlxxJyVVJIgtCEcJqWLVtawxsPEBzAK8MyeIRAtIHHiCVm4F7ktElMTDReJBAKXHO74F548FSXaANPJ+SLgZeKazlzPLfkMc7BXPMDNW/e3JxzFWOsPYOoYRnEIXhcWd5HOB9VN8pctkLYcLBkyZJS60folGuYFvq5CmTIZeOazwbXIf5AeIGYAr4WP9dnoZ9l5513ntU03926dTPiU0kGp512mlvlMbzjEG2QV4dGAiRAAiRAAiRAAiRAAiRAAiRw4ghQtDkK9vixDm8NeIt8/c3X8vhjj5vQGwgr8C6xwpTwY/poQqE8Tck1ia5V2hveJK4Ciqf7Knru448/lvr168uGjRvMepBY2fKUscaw8tmgTDkEHZglbOAaxCQYwn0QcoMQG8wRXjkvvfSSuQbRASXSq9PgNYTqXpZZIpJ17OkbXi/lmbVOVNhytZL3WR5I6ANxBMIK9sjygPr3v/9tM3MdB20kai7L4HVzww032JexJiQlLs+io6PdLlsiD7ypXK3kGlyvsU0CJEACJEACJEACJEACJEACJHDiCDjjN07c82v0k+EpMfSGoQJPBdivM381361atTLf+IPktnPnznX7WIl67U7asEJpLMHH9drxaHfu3FngiWGtByIFPIksw7Fr2JR13grtQTiY5cFhJTFGaBgMSXJvuukm80Fen+o05PuxBBskjYZwBJEIotSxGPLHwNasWeM2DLx6XA2hVVbfTz/91FwCD2v9qExVllnCjqfrEKJgKB+O0uFYE77Ls5JzQylyWHniUHnj8RoJkAAJkAAJkAAJkAAJkAAJkMDxJUDRphK8kcjWylVj3QahwvIciY5yejbgRzo8IWAIj0I+FZRmtj5W4llrDHxbHhwIw9m/f799yQqpsk8ch8b9999vnoIcMElJSaaNXDCwTp06yaZNm9w+F110kblmeeJYeVTABYl14bVzvGzXrl3mUeDfs2dPU/0IFZ1K5v+p7Hzat29vboEIh0TNMAhsqLJU0ix+SGT81ddflbx8VMcIh4OBtVUyHrlnyjMrmTT6wCsHYWww19w45gT/kAAJkAAJkAAJkAAJkAAJkAAJeCUBhkdVYlteffVVk0sGnhTIe4K8Ipa3A/LJQCSAwWMCFX2Q/BU/8vGBhwk8HOLj400paKuUtPV4VBqC1wo8WpAnBiIBcsEgZ8obb7xhdTsu3xdffLHJZQMvFVQbevbZZ+18NihrXdJwDjlkINrAowRrueuuu0z1JSuUCt5IWF/JZMolxzrSMYQg11Ax1/7Im4Oy7DDsDUplgyM8TOABg+dDuDj//PNN8uizzz7b9fZy26jW9Ntvv5k1IuQL4hVCwKywKdebkdQYVaqQrBqhc/997b9mHhCvXPPP3HnnnUYUs8KcUI0MoUooIz58+HDXIU3uIoyHsTFvCHtYC5JCQ9DBe4IS8xjTMpQCR+JsvK8WdwiG/fv3t7rwmwRIgARIgARIgARIgARIgARIwIsJ0NOmEpsDLwckok1JSTGJWi3BBjlLvv32W9u7BkNeffXVAsGiXbt25gnw0EF4EYQQT1V5ICig3LIl3KCUM0QbT8lgrVCqkolsK7EUu6vD4TBt17HQtrxFEFaE9VqJil0T9lqDWOcWLlwoVkgOkjPjXohVEDYgGiBMDG0IByXzrVhjlfXtOj8IH54+YIV8PyjPjapWEEN+/PFHwRohogUEBJjhsReoaAWz1m8xxTnXNo5hSLKM/ETW/kAwgXBjVakKCwtzdtS/yHmERNWoRgXBBHPF3lueLlbyaDCxzuFmzAvHnt4PCHcQ0zIyMowIuGXLFpNDyBKw4KGFd8vVILaBiSXY4LnwzvG0Ptf7LCauzF2vs00CJEACJEACJEACJEACJEACJHB8CPgUqR2fR9Wup0BQATok78WP9PIMIU4IoyooKDBihRU65ekeiB7wxrH6WqEwnvrWlHNYP3iBEzySjsSrqtaFMCZ4t1iJdiE++fn5SWBg4DHNAZ4tkZGRZozWrVub6SJ/zwUXXOBx6hCq9u3bZ8KaIFYdixgClhCcrKTCWCPeQwhS+GBsVOYCb3h4QdTBfPHO1YZ3ySNgniQBEiABEiABEiCBWkQg89dfJVf/v2OkerDTKk+gKCdHkt59V0K7nSFBfUpHCVR+RN5RFoF8/d2a8uUXEqlFevxatCyrG88fAwEUumF41FECtJLNVuR2iBSe8th4uhdeEK4ViDz1qWnnsH6UkT7e5ur9gmdDaKkKQ0gSRLj333/fHg5eN2UZvHOQz6gqDCwtwQbjlVyjp2dgvjQSIAESIAESIAESIIGaQSBN82hmqUc2RZvS+5Wk6RA074FE3Xdf6YvFZ7JXrZZkLQiS/ccf0piiTZmcyrtQpN79CRqpEKJpJcKK85d66p8+aZKkjP1KfB1+EjVihKcuPFcFBCjaVAFEDlH7Cfz555/y9ttvmyTErrmMsHLku2FFptr/DnCFJEACJEACJEACJEACJ5ZA+qzfxBERLlKOaBN0xulS/9EREtSl44mdbA1+emFOtqRNnSp+qKBbjmgTNniwiJ+/hF92aQ1erfdPnaKN9+8RZ+gFBCDU/Kquqq6GfD233X6b3HD9Da6n2SYBEiABEiABEiABEqgtBDR1QcZPP0nu+vUSoAUu/Nt3kCytqhp+xRXiWzdSUj//XESTTfg3jpWgM7rJIf2hCwu/9DLxLa4mm6LVZDVmXxz1G0ioFoTAfa5WdOiQpE2eJIXxezScp4/rJbud+/dmyZzzuxTq/ycN6NDB6f2gYfEVtbzt2yR92nSR/Fyd26WStXqNmV+o5ua0LOuPhZK9bJk5DO55tq7HWeADJ9LGfSv+zVtqWH6BerAskpCu3SSo17lIBmndLoXJSWb9hbvjxdE0zjDyCQk11zOmT5f8vfsUlEMirhksGVrgI3/bNgkdeKn4N3N65KdPmya5mqPRERomIX176/MOe6pnz5krOdq/MC1VCtQLJHWMci+2iOuGiI963+Tt2CmZs2dbp6Uo1Vnx1T6hjUJNNZA5eYrkbdksPlokJkxzRvppugtYYXKKHFLPkeBzzpG89ev0eVsldMAFEqi8K2L5Bw5IxtRpRiwK7NLVviVdC7bgWqQWDamIGY46xwJNiRBy/nlSlJklBfsPSNjVV5nbsfaAtm0EewTLWrzEvJ8RNw8VH1/nz/vy9gL34J1Lxx7s3CEFmm7BNyhYgs8+R4J7dDd7nX8wEd0ke/Uqm7Ujso6EDRpkzmctWSq569aZto+maMC+OCDwuFi+zh97WqShfv6tWkvI5frfhEYNwPI3rJeMRUsk7MIBZh5FGVlG+Ck5hstwJ3WTOW1O6u3n4itK4JD+DxsS/SIfDipRoUQ7wp680ZDnBjmRkMPmSEmHvXH+nBMJkAAJkAAJkAAJeAuBhKeeklT9IW9Z8KmnStbatdL0x4kS0LqVbNeqr/maNzGobVvx09yF6VqYAxbQtJk0nfqzKgGFsk0LWhRkZlpDSNQdt0vUgw+ZY+Rf2XXlVZKrP54dWoEW/XBvwcEEabFkiekDwWL3fffa96NRR8WG+q+95naurIPcTX9L/I1Dzdh4hq8W7ijSYhUhKso00EIbsORRb0niRx+7DdHgmaclYsh15tw2zZkYoOH+WLtlrnMo0HyKu9TrAiwsC2jcROK0KIhPSLDsf+B+yVy02Mwh6rbbJEnzQVrWXMUDvwYNZJ96r6dr8Q7LQpRbjKYjwA/9hOefk4yZs+zx/VzSHjSdMlmFsCjJ0nv3P/GEuR3ziNB8kw1cq/Aid+gNN0iWVqO1DOM0GTdO/Bo3FghjO68cJMFa9da1T8PnnpfwwddYt5T5DXFle98+EnhqB4n9bIzph/3d0q2bsu4msS5CU1mDQEzZeeWVkqdCh/U+4N0qULGumVaFxfu0WdMy1LnqKqn/r3+ZYQ6+/JKkfPW1tFLBDeLVkfYCgs6OQVdJQeJBc7/Fsu5tt0ud4cNk12WXSUFycinWgc2bSyMttAPD+5L63XgpUjES76zru4Lr+XviZfe1Q+wxcA5cY78ea4Sl1K+/koSXXi7Furnm5PRr2RLdacUEkDrlsDRKLCRAAmUSgADSuXNnUw2suf4PlrcKNlgA8twg8TAFmzK3kxdIgARIgARIgARI4IgE8rbvMIJNiHoftFTvmhbqdZ2f6PRAMDerp0vz+fONgJK9caMKB3WlleZRiRo2zIgwpq96okB8ab1mjRF6IEQkffyJ+VGLMTJ++tn0jRo+XJovWihNxowxx66TO/Dqq4If1hCKWqmHQx31dkiFV4qKDBWxlNGfmB/Wjd56yzyjznXXuf2Yxo98CDaYW/M5c6SliiMQChJff8NteAg2jUd/Kq2WLzd9MQeIErDkjz40Y8a++aa0Wr3S/IjPjd8taRMnmOsNR70t0Y/8w7SztFpqM113k3ffM8fZOh4MAk1rFVTw/KhbblGRZ5Fk/qzCl1r9518wrCFo4cc/uFsfCDawYBXQrHPmRIk/6dOmGjEmYuBAs5/1HnvMzDn5o4/cehaox02zn6dKy3lzjXBy6Ben95RbJw8HEKdCz+8nmUuXSUGS8z3J1PcBFn7p5R7uKH0q7YcJRrCJ1vCvFrp+MMK7VRk70l5kqmcOBJtIFfKwVxYzCDawuClTJE49v2AQ2KzrlmCD83UfeNCcj9X31ZMlf/iRYQvG+G8HzCGEZUz7xa27Q4VOXMd7BTs0fYbbdR44CVC04ZtAAiRAAiRAAiRAAiRAAiRAAiUI5G1y/liOuORSE0qE0I2wAf1L9Dp8GDHkWvHRf+irM3SoNHp7lPgUh4LAewLhQJm/zhD/4hCSvK3bzI05652eK+FXX2M8EBCSBMHEMiSEhRdOkHr4FBw8INmr/pSAYk+EXA3xqYjlaBiLI7qehPbrZ54Roc9yNUsUwDOw5pxtW9SLqLUReizxAf0xL4TP+Kjneei5vcwQeTt3me/s1WvMM3zCQiVbRYGAmFhzPn9z6TmG9T1PQ5+aSaCOBU6BGnIGK1SvDYRnpalnkyM8wpxDuFRVWd5aZzhPneuHmP2sM/R6M3SOi+cNToRrSBJCtiAGBauXTM6aw545R5pL2CCnOJOhAh8sE94xaqHlvDemQ/GfnA0bTAvvEkLPUP3KX8O4KmNH2ougjh3NcOkqnB38178lXQWqosyMyjziiH0tpmCMMEEwh1l7YA0Qfs3V5jreK3gW5W10rt+6zm8nAea04ZtAAiRAAiRAAiRAAiRAAiRAAiUIFB5KN2ccsYd/NDsaOcWIEl3NYWA7p/gAcSe0WJxBLpe9jzzivK7hRQg7gRXm55vvAusZmhPHMj/9kZ63yymGFBVfT1dvBHzcTEOcKmL5aYcksEljuyty6uAHsmUQlWAmZGn04bAlnEMYlWWYl2VFvj5W03wXpmcY7434O+50P5+T63aMg4DTTjXnEMoTet75po3S0bs0LMiEh2lYVVGBk49PMSfT6Rj/5Bev06+hcx3I/wJBBKFHruZTvHfmnEvOHtc+ZbVDuvcw4lX6rzNNSFX6jBkSpnmKrPxGZd1nnS9MdYaXOYq9h3DeP66Jvg+7rS6lvouyDu8RLh5pLxAKFvvJx5L25VhJ1xw8qd//YN6HWM29FFhORdxSDy7nBJiCrZVjx2Ju7YF1a2UFKeu+k+2bos3JtuNcLwmQAAmQAAmQAAmQAAmQwBEJ+NWrZ/rkrt9gJ30tLP7h7+lmhMeUtMQ33jQ5appMmmhys2Tqj/g9/3CGCaEvcrnA8vUZAZ2cHhCFGU6xCOd9GzqvB6rg02SS5odRoaGyFhDTUDJXrTL5UOC9gZAm1xw7Ac2amyEjNW9LPc3fcjQWoJ4pCIdqriIVRIHyzBFRp9RlhChhTi1mTBdHbGOT1Hhrr96l+jlCQ0yy4qLC/Eqz8It1CmNZWhI8TMU3eBEhd0zImWeUes5Rn1C+dbSSUpKGDWXMmGnWFKY5YipqfrpXsHxNHO3XoqVpF6roZpuG5MEKXML0srXKratVZC9CzuqpIW49zTuRuWSx7Ln9DhVxvrTzJPmqoAbL9eAp5fqsstoQmqwwMUdUtIA5zNqDsu7jec8EKv9fvedxeJYESIAESIAESIAESIAESIAEag2BQE0eC4+U5E8134avQ4ocvpL2zTf2+lAdJ1vzjuQWe8Wkay4Qh4owwT162H38tIoSKk9la1WjQk0im6S5XWCZxcl3Q9QLI+mzz+TAM8+Y6kJ5e3ZL5rLlhz1h9Ec68rskaZWqfTcPk5D+A8ShP+zhkWElCbYfVkYjpG9fk0B43/33S/iFF0rm3LluPeH5ggTLKeO/lyL1mAnWH/M+/vozURMWu1aXcrupxEG4JvhFEuY9t94mYZdcJIHt2mv+n4MSrtWhfDVkDPlkcjSECpaxYL4y2yFhes0yf022CktXToFNmkrSl1+Y46x1ayXg99/teaCCV5byTPjHoxKiok6hVsMK63Oe+CoTVFEqOLDf3Ic/eeq9gz2BABEyYICEX3ShJL77rhwcOVJyNQwn87ffTd9wDX+rSkP5a4g2eA7en4oyxBzC+p5v9mH/Px8zYXYQTRC+Znuk6PsAz510zT2UpGtBPiJU1YKl/fC9hiHdIOXuhYYq5fy11oSh+UXVlUL1hMoqzilU5DgsDaDqFxJJ4zkHX9GEwR00PE/FxAgdH4aqUKJeUHm7d5tj7G263uMf11hQOSt84GVGtNk7/DatgNVX0vTdgmEPaJUncHhnKn8v7yABEiABEiABEiABEiABEiCBWknAJzRUGr32X9k74lFJ+O9r5gd4YJs2JsEq8tXkbdok+4qrFQEA2vDacBVtou65Ww6++JLxrsEPeCTjPfDvkZI6YYI49Ad0lIZOIeksxIR9zz1rvHLwozxr6VKbad0HHhBxOCT1228lY+VK+3zE4Gu12nihFGipa09WhPAf9YCJvGWY5grZKGmaXwU/wkN6qocFTEs1G1PvkEZvjTI/zpFcGB8YEhOXKTgUe3yYjvoH/WJUpEh4838m0bJ1PrBdOw2H6ij7HnvcOmXWioPWLqIN8uxkasnxhJdfMf2QbBnPRzJihP9Y84i+9z7jZYK14APzU0+OkJgBkqwhP1biX5xH4uQs3RMkcW6uog08Vxq99JIc0HkiGTQs+qabtJT2INM+0h+EkaGql0fTMuWW15R/23YS1Kq1ZG/ZLBFa+h1hYJahLLmPegl5MofuFXLYICk1hLysp582eYQwFgQTy+recZvO46AkaeJm5Bmy+oNdxJVXl7sXQVqqHXmD8D67GpJtR+l77moNXhwpiVqhLGXsV2LVBAu76BJTsn7fiBGuXU3CblRZMxXFVLQJu+oKyd+6RRK/+MJwwLsP9pb3kE+J98cerKzzdoeTs8GS3yfnvnPVJEACJEACJEACJEACJEACFSFQVKS5aJIEeUbir7/eVMFBBSUk5K2oFWqOD3icmPAkTS5cpKIPSllL8Y9UhPsUpaaZ5LemIpO/w2P4D8o1F2XniI+GbuF+iAA7Bl7icRpIPtxizu/2NSvZbPafKyX+rrtMZaCohx+2r5uGegMVHkiQQvUqQglzzLeyBnGjMD1d87hEaLnv0ErdjrLZos8GW7sNTiXm4XzGIRVEtIS55uiplOl+5idhP+t4ZFzWWOla2QnCmidDdaQGWuXrSLatT1+T+8dTP1TUQoJmGJIyS0amWdue4Sq6aU4bU/Lb5caCtDRxqDcU+vrkF2imaP9S6ylrL/A+4n0o8tP3EFVnLQHPZXyriSpoPvqMo9pPfa8LklOd75I1IL8rRQAlv+lpUylk7EwCJEACJEACJEACJEACJHCyEEBZ7cJDqVKQcFAOTZ5sBBt4wlRGsAEr10S08OBxT+Or2o16xfioKAQrb2yrvLXpqH8csY0k7ttx1qHbt68KPzD8QM9XcacgK0uyVyyXlOL+oRoqVcpUHEGoUeWlmsMjoYKWAwLVUZhrXiDXdsmhjuUZEMqMIFVy0CMch5x/vsS5VPZy7e6Iqphw1OSjD6QwTwUWD4a9tMyIKOUIKegHwQbm7Guapf6Uxcm8jyrWVMSOhpU1Lt7rY7nfGudk/6Zoc7K/AVw/CZAACZAACZAACZAACZCARwJ7NbwJyWotC+vVS5P1PmcdnvBveNsEFldjKmsyGZoTZv9TT9mXEaoS88K/JLCDs9qVfYGNcgnAoyewsl49JUZE6BSNBCpLgOFRlSXG/iRAAiRAAiRAAiRAAiRAAicFgZyVK6Qov1B8tWqRf7MWGu5TukKUt4NAeEve5i3iG+gnvpHRJs+NFZbl7XPn/LRSlCYbLiooEL8mTYjjJCSA8CiKNifhxnPJJEACJEACJEACJEACJEACJEACJEAC3k0Aos2xhCt69+o4OxIgARIgARIgARIgARIgARKo5QTyDxyQrCWHq03V8uVyeZUkgITDeD+QlJhWMwkwp03N3DfOmgRIgARIgARIgARIgARI4CQmkL9tq+x94EHJ2bZN/GNiSlUXstCgGlV5yY2tfsfyjapWaeN/kOBzzymVKydt3LdatjtH6gy75agekR8fLylffiGRQ4bYJaOPaqBK3pSzerVkLVps3xWmVbr8Gje2j6urkfnrr5KreZQitRx5VRgSUcffOtwMhSTaDV95RZCgmFZzCFC0qTl7xZmSAAmQAAmQAAmQAAmQAAmQgMlzsmvIdVKQmSkNnnlags86240KvCoO/uc/kj53nikx7RcZKaH9+0m9J56sFgEnX0WGg6PekvphIW6iTWFenhz490gzt4jB1wgqZ1lWpKXPE154QVCVKeyii6zTpb7TJ02SlLFfia/DT6JGjCh1vbpO5KxfJ6nfjZNCLa0NzoHt21VatEn54gtJfucdj1OMm/ij+MWWFoHSJk6UrKVLq0y0ccQ0kqZffyPpv86QpM8+k/xhw6TJ+PGlyqh7nCRPegUBijZesQ2cBAmQAAmQAAmQAAmQAAmQAAlUjEDy52OMkND4088kuPuZbjfBs2bXNYMlN363RFxyiQR07CjZixZJ6vc/SNjFAyW4R3e3/tV5gOpWjV5/XQTePi6CDZ5ZmJMtaVOnil8jLXVdjmgTNniwiJ+/hF92aXVOtdTYESqK4QPPlz0PP1zqeoVOZGWbfYq8caj4BAS63eIbfFjAcrtQ1Qda4jygU0eJ0o9vcLAcfO89yZw1S0IGDKjqJ3G8aiJA0aaawHJYEiABEiABEiABEiABEiABEqgOAocm/CghnTuXEmzwrLRvvjWCTf1HHpU6w4c5H6+hNrl/b5aANq2dx/o3e85cyVyxXHyCgiW0b28JaH+4BDhCmvybt5SiogLJ/mORhHTtJkG9znXzzshavESyFi4QR90oCWjbxh4XjcLcXDmk3h2W+QS7CxYYP/9gormcvXqVpI753LQdkXUkbNAg00Yeltx160zbJyBAPV5SxQGBx8XyExIkfdo0KVJPH/9WrSXk8ssEQhEsf8N6yVi0RMIuHCDpv/0mRRlZRvhxHQP35m7dKo7QMAlRBv7NW7iMXnXN6PsfKCVaWaPDKypt8iQpjN8jQRq+VMoKCyXjp58kd/163aP24q/7lLVggYRfcYWgDDkM4WmHVAAr3B0vjqZx5ppPSGlRKPzGG41ok/b99xRtSoH23hMUbbx3bzgzEiABEiABEiABEiABEiABEnAjgB/oCNcpy2MmY8F8cYSESLh6d7iaq2CTpN4vCJWxLPHdd6TRW29JaL9+5lTi629IQIsWkrV2rTlOGj1a6lx8sdR/7TVznP7jj7LvmWes2yWgaTO7jYaPetYkf/KxOZefkiIIz4LXimWpGu5UkJxsDjOXLjOCEg4Cmze3RZvsRQs1PGm8FKkAZIWB+bdtZw0h+XviZfe1QwTjWxY8/juJ/Xqs+Pj6ScaKFZLw39ck/ZdpkvXXX6YLQriaT54sfi1bmuN0bafPm+e8XfuGnHWWxLz/vi38WONW17fxilIuuTt3mD0r0HCqkiwTlHOqhohZFnzqqWZfgs85RwJUtEFJ8F3qjeTKIWXMFxKne1SyRL0jIsKIfXl79ljD8bsGEGD1qBqwSZwiCZAACZAACZAACZAACZAACYBA3t59BoSjYUOPQPJ37pIAFSUsj5OSnQr27jGCTZB6pjSfPl2ajBljuiT+xynIWP0h2DQe/am0Wr7ciBmp8GhRMQaW8L+3jBDTfMpkabV4sQS2doog1r1IdNt8/nzzCevVyzptf8dNmSJx6l0Ci7rtNrtvo7Fj7T51Nckyxogtnp99obiR/OFHRqio99hj0lI9TyIGDjTiTMa0X9y6OqKjzXWsBXZo+gz7OgSa1irotJw5S6JuuUUyNYws8+ef7etV1dj70EOy7557zOfA40/Yw2b89LMRbKKGD5fmKlJhLyDgWJa3fYcRbEI0pA1rbKGhWvmJTg8lq0/yRx8aDrFvvimtVq80OY4QGpc2cYLVxe3bT98bJK+m1RwCFG1qzl5xpiRAAiRAAiRAAiRAAiRAAiRQLoGignzRuKYy+2Rv3GSuhWm+G1RDCjrjDCPK4Ic+kgNbFtS2rfHmQeWp0HOdwkueCkLG0yfxoISop4dfi5Ym7CfsCmdIk3Xv8fjOKfaeqTP0evGtU0fqXD/EPDZvrTOkyppD+DVXm+vwTIIHUt7GDdYlE8aVvWyZpKkniyM8wpxHuFRVG0KgrE/hoTR7+Jz1Tk+m8Ks1SbOvn9kLcLcsb9NG04y45FKzBoR2hQ3ob10239mr14gjup74hIVKtoasBcTEmvP5mze79eNBzSXA8Kiau3ecOQmQAAmQAAmQAAmQAAmQwElGwL9RjFlxwYEDHlfu36yp5Kz5S4oK840QUKqTVkOCOWIOe+qgZDgMlZIcxQmD/YrP4XyRrw++jBWkHjLffrGH88v4u7SLu1X7V4GWssa8IXbA/Bo615CvAomrWWtzPYc2SonvuvJKE3oV0LiJGLFLz/vkq+hVxRar4WUlEzHjEQWH0s2THI2dQgsOwD1v1y5zvtC6HutcG046Gh3ui+PC9AxTISz+jjtxaFthTq7ddm3gvQnU0DdazSFAT5uas1ecKQmQAAmQAAmQAAmQAAmQwElOwDeyriEArwpPFtylixEiUr86nAjYtZ8j1vmjP3fVSvt01oo/TbuskCu7ozb8GjQwh7kuHi0Fhw576Lj2La/tGxTkHOcoPUL845pIniYgLkhyhgtlrVptxvMrXl95z8a15I8+MpxazJguTaf/InGaD8eTFRUnNi5Idwosnvoc7TmLZf76DfYQhRmHn+NXr545n+t6vYQoFaAiHQyhbgj1sj71X3zRnHf9A0+qjJUrxb+CjFzvZfvEEaCnzYljzyeTAAmQAAmQAAmQAAmQAAmQQOUIaAnnutcOlmRN0puroTEo5+xqkcOGS+q47+Tgq69K3oYNEqyVnwpzsyXnr7VS7/HHJUCrTsH7BPeLjpW3b7/Jo4JEw+J75H/TR3Jb5FhJX7hQDr7ysgS1aasJg8e5TsEkFs7V6k2wfPXsQDLhdM1jAwvtd74myA01H3i4pM+ZY8YJ7nCqFKhgEXH9DaYfKjuJer3k7d5tjnN0rel6n39cYwns0lXCB14mSGK8d/htEnJ+X0kb/73pF37Rheb7SH/84+JMl/TZsyWwSVNJ+vILc5y1bq0E/P67VtTqa44Di0WRlE8+0fCxTBWJkjREqZ8JDTMdKvAn9fPPteR3gFvPiCFDJESrRSEh9AFNNhx5882St2e3ZC5bbsK40DnwjG6mnfyp5uPxdUiRw1erg7mLceE33GD2Ys+tt0nYJRdJYLv2mvfmoPJxhlS5PjRtnHOfrApdrtfY9l4CFG28d284MxIgARIgARIgARIgARIgARIoRaCO/kCH6BJ/+21S/4UXNCdNDy3/HGX6IQlw3PjxkjBypElia1UeQuJhVFxCBaaYUaNk/6MjJFnFHVjY2WdL9JOHE+Sak65/VNxxtfrPPCf7H3xQUrQKFCxy6A12pSkcZ6oQgkpNrrbvCef4zX6aYpfWbvDiSEl87TUzjlUDKuyiS0wp630jRrjebq/FVLFS0Sbsqiskf+sWSdSKS9lbNhtxo9FLL9liik+JOduDFZ+P0DwymX8slISXXzGX6mipcVSPQjLioqxsW7RB3p4oTSIMsWT/88+ZvgEtmtvPscf11Ch+1sH33it1NWTABSaHTfR990niu+/KvueeNZWjwlTIyVq61PRHSFWj1/4re0c8aiphISdPYJs2JvGwT7EHEMSlGN3rhDf/J0kff2I/J7BdOwlSwQ6Wt2OnZKgnDvYEoVFhF1ZM2LIHY+OEEvApUjuhM+DDSYAESIAESIAESIAESIAESIAEKkUgZ9062XPPvSafCTxWEOJTyvSnXr56hvhqqWdP1aQKNS+Mj39AqdLQpcYp40RhskotmgAXYxdlZolPsIY8lSWWlDEGTqMiko964/jWiTAeOOV0LXUJuXsKklPFT6tEHY1h3qIeLEi4bLchiJTwOsJzig7oPLXMNvpWpZmxU9OM8GYqdPk77Fw95jm6jwVa6t2hwlz89debKlmo6lVyHkh2XKhhXK4cURJ824ABZpgQ9dyJ0SpTlsBXlWvgWNVDIE49wijaVA9bjkoCJEACJEACJEACJEACJEAC1UugsFByt2yV/O3bJKT4h3n1PpCjH28CuX9vlsJDqVKQcFAOTZ5swsngjROj3jkVMQg5Weo9FHhqB3HENq7ILezjRQQo2njRZnAqJEACJEACJEACJEACJEACJEACJOBKYEf//ibhsnUurFcvqachcVYSY+s8v2snAYo2tXNfuSoSIAESIAESIAESIAESIAESIIFaQCBn5Qopyi8U39AQ8W/W4qhD2WoBipNyCRRtTspt56JJgARIgARIgARIgARIgARIgARIgAS8nQBEmyPXdPP2VXB+JEACJEACJEACJEACJEACJEACJEACJFALCVC0qYWbyiWRAAmQAAmQAAmQAAmQAAmQAAmQAAnUfAIUbWr+HnIFJEACJEACJEACJEACJEACJEACJEACtZAARZtauKlcEgmQAAmQAAmQAAmQAAmQAAmQAAmQQM0nQNGm5u8hV0ACJEACJEACJEACJEACJEACJEACJFALCVC0qYWbyiWRAAmQAAmQAAmQAAmQAAmQAAmQAAnUfAIUbWr+HnIFJEACJEACJEACJEACJEACJEACJEACtZAARZtauKlcEgmQAAmQAAmQAAmQAAmQAAmQAAmQQM0nQNGm5u8hV0ACJEACJEACJEACJEACJEACJEACJFALCVC0qYWbyiWRAAmQAAmQAAmQAAmQAAmQAAmQAAnUfAIUbWr+HnIFJEACJEACJEACJEACJEACJEACJEACtZAARZtauKlcEgmQAAmQAAmQAAmQAAmQAAmQAAmQQM0nQNGm5u8hV0ACJEACJEACJEACJEACJEACJOBlBEaNGiVdunSRkSNHytKlS6t9dnl5eZKQkOD2ycrKqtBzd+7cKZ999lmF+rLT8SXgU6R2fB/Jp5EACZAACZAACZAACZAACZwsBKb/lSCz1iRJQnqedGkWLg9e0Oy4L728OeTkFcpj322Svu3ryqCuDatsbvP+TpGZqw/a4910bmNp3TDYPvbWxv7UHHlv5i656PT60rNlHa+cZnJysoSFhYm/v7+ZX3Z2tuATGRnpVfPFPGfNmiUzZsyQadOmSffu3eWiiy6SK664Qho0aFDlc507d64MHTrUbdxnn31W7rjjDrdzng7mzJkjI0aMkCVLlni6zHMniEBcXJz4naBn87EkQAIkQAIkQAIkQAIkQAK1nMCT4zfJpzN3mFWGBPhJcnrucRdtjjSHeZtS5Nu5u2TumsQqFW2WbU2VsXN2SW5ugeTpv5Of3iK8Rog2O5Jy5MMZ2yQ8xM9rRZuHH35YWrZsKRAkYDiGYPPyyy+bY2/5U7duXbnmmmvMZ9u2bTJ79myZOnWq/Otf/5I+ffrIwIEDZdCgQRIcXHViXps2beS3335zQ5CSkiKrV6+WevXqyebNm6V///4SEhJi+hw4cEAg9jgcDrd7eOA9BCjaeM9ecCYkQAIkQAIkQAIkQAIkUGsIwLMGgk298ED55qGu0rFJ2HFfW0Xm0LtdXXngstZyTuuq9dJ4+MJmgs83f+yTh8esOe5rr80PfPzxx+Xyyy+X+++/XxKTEuWnn36SZcuW2UtGKBJChXr27Ck+Pj7m/Pbt22XFihWSkZEhTZs2NaKJfcNxaLRo0ULwufXWW2XDhg1mzmPGjJHnnntOzjvvPLnkkktk4KUDxc9xbD/Rc3NzBR4+sKCgICMI/f3333L77bdLo0aNjDjz7rvvyvTp040X0LBhw6RXr15GzDkOGPiIoyBwbG/EUTyQt5AACZAACZAACZAACZBAbSXw5vQd0qGx81+wp61KlEaRgXJ73ziJDnX+3+5Xftomhbr4lvWC5Lz2UfLFgn0GxfDesVIvzBnqkVdYJGPmxcvqHYekfri/3HB2rHpoOMeECPHx77vl8i4NZLF6cqzcniqdNOTo1t5NxOH8bSoLNCxn8vIDEuDvI307RMv57aL0h+th4sc6h8Mjld9atzvddBh4RoxHwWZPco58uXCP7DqYJe2bRMgdfRtLQPEixur5nerx4e/rKw8MiJNvFu+VjXsyZLiu85QYJ4vyn+68Wt4cNu3LlO+X7beHScrIs9toLN+WJtPXJsr1Z8XI90sOSHpWntzat4k0iw6y+32+YK+siz8kEcEOubJrjO59qH2tqhrr4jPkqz/2qAhRKDf3aiJ/bEmWKH2frlauCMGat9H5A33YObGyJj5NflmdKJdrmNd5KkbBjvQ+oA/Cx6bqfQ0jAqVzXOk1lPdO4v7jbe3atZN+/frJBx98IPv37zfhPw0bOkPbrrrqKklMTDRTql+/vnz//feyeMliuebqa6RHjx6CfvAugadLRW3BggUSGBhoBBCEZFltfEMYwXd6erpkZmYaUQjf6RnpkpXpOZ9MTk6ONGvWTK699lrZuHGj/PXXX/LUE0/IY489ZoQdiCt9+/aVm2++uaJTtPvt2LFDOnXqZI4hEL3wwgumjdw2YIHsKN26dZODBw8ar5977rlHnnzyScPy008/tcdhw3sIULTxnr3gTEiABEiABEiABEiABGo4gTd+/FsaRgXJ7sTDP9bGzNopy1/uJcEBvvL+1K2SU1AoDSOD5OPfAmXtjlSz4k9nbpf1b/SRfBVs+rywWLbuO2STeO+XbTL58R7SvUWExKvQMWrKZvlx8T7ZdcApioxfIDLzr0QZd19nc8+sdcny+ewdpv3xjO0SGugn05/qYYQfZLM81jnYEztCY39arunRzUNelM37s6TfCwsNC+cw8fLZ7J3yx796qlDjI9+qGLJKxQmEFe1OyZJxGmYE+2zWDln+Sm9pXDfQedsR/pY3h3V7DsmHuh8w7MlZ7aLlym6H84zM3pBkWE9YtFd2J2SYfggb+vWZnrYI9dX83bJ6a4q59vZPW6Wdik8znuxui0/mwjH8+XP7Ibn8lcWGg78qbz+oeJOfXyidWkYa0WbuhkT54Odt5vr+tByb0zfK65WbOsgwzaNT3vuAqb0/e5e88O0Ge5Z1QwPsNhpHeifdOh/HA+RfQX4Y2OLFi8335i2bZdOmTTJ//nxzjBwye/fulV07dxnBBSLG2WefXeHcNxA5Pv/8c1m5cqUZ73j8gYCDD7xjjka08RQehXkjBAsiFjxxYBBx4PFz4403mmMIYTTvJMDqUd65L5wVCZAACZAACZAACZBADSUAwearh7rJzvf6y5Bzm0hyRq58Om+38XbZ8V4/Obt9tOxPyRb8OF7/5nky+BxnH3iewMMEgs11veNk66h+8uNj3Q2Ff//wtxuNpNRsWfRSL/nr9b7SpnGEzFl9QNbvdQoLz17RQvZ9OEA26Nj44Z6Rky9Pf++8Hx43VTUHtwm5HDylz+r6+AJ5Yuw6c/ZpFQRwjM/cTU6vkBcmbDZCycOXtzZrOLdDfSOMfKliDeynEd3k0avamPb8KZ2hAABAAElEQVSCdUky+/mz5f07naLUTD0+klVkDkg6DBb4lGf1IgLMHD+/v4vp9q2KOJbNeOxMw3qJinJX6z5u2J2mXlJ7rMvH/P3ipC1GkBl1WyfZqvO8+bw4F6FL5KnLWskvKiLBJs6Pl0/v7WreKRzPWe9kXd77gH6v6V4EOnxl5jNny8b/nS+tGrmHsVX0ncRYx9OQ0wZhPUOGDBHkjoFt/nuzpKamysUXX2w+0dHRsnu3eqZpKNW9994rb731lnTs2FHee++9Ck0V+WgeeeQR+fLLL2XChAkyb948Wb9+vezatavKPghTgrdLhw4dJDY21qzn448/Ns+q0CRLdCooKDAeP/D2wQdhYmXZWWedJUhADCEH66J5JwF62njnvnBWJEACJEACJEACJEACNZQAvGj6aegT7L4BzWScemOs1FCbknaf5jupq8leHxnYQnppCFNggEOWbXF63nRTr5rZG5yVh5ATZvMep1eNNcYAFRyaF4fpDDm7kYwcnyYrNFSqfaNQDX8QWamhVRA3ENKDBMCbNYTHkx3LHDyNh3PtY0MlVSsx/b0/03jLtIgJlTbF4V3RxV4cq7c7vVMevqi58Uq5X1nMX5cgK9Rr5dZesW5Do4oR1tVUPZggXnRuEu523dNBRebg6T5P527V8DaErl14Wn2Bt8vq7Yf3MldDluZqIuM56vFStzgEDuFSVWUbdqWZ/bu2uzP05159n95XzytP1kLFlks6R5tLo+/tLBEhTo+Z8t6HA2l5kpmbLxDNTmviDIu6uU8TWbb5sDBW0XfS05yq+1xUVJSb1wxCnuBRgjwxCJ+CIZwJXiX33XefPPDAA0a4gSgCEacihjClqjaU5UYeHgg28BIaMGCAPPjggyavzbE+a+vWrdK2bVt7mKeeesqEQ9knXBoQvOCx1KpVK1Oa3OUSm15EgKKNF20Gp0ICJEACJEACJEACJFDzCTSKPlwJpnl9Zzs5M7/Uws5sHmHOQXyxBJjUzAJzbsTna936I8TJ1VoUj4tzrRo4f2wnpRdIbkGRnPnkfOPJA7Em0N/X/CgPL3C/3xrrWOZgjVHy+0bNwYPPOM1D86CGON15fhMTyuPaLz0r34RtWTlsmmqOH1hyRmlOPTQUCBYa6BBLvDAnyvlTkTmUc7vbJWsP3U7qAUK8zntugfGEiVAxqkDDlmAIX6oqy8wpkGgVAS2rr+IRhCNP1sUlDG1gZ2eY15Heh2TNkQSLq3/4GSXXW9F30tOcqvucr+Y8shIN41kQbF566SUjzFhlrnfu3GnytbzxxhtmOsgXc/fdd1f31DyOj/LfU6ZMkZkzZxrvIFSPQiWpU045xWP/yp7s3bu38QDydB/CxmABAQFufSZNmmRELeTlgZcOzfsIeP5fb++bJ2dEAiRAAiRAAiRAAiRAAjWCQJqL8LBUkwXDmkSVzsESFuQotZ42sWHyq6bPsPKRlOpQfMI1ae7SYi+euOgADa/aawSbkUPby+2atBd22qNzi+8q/XUscyg9WsXPxEYFy997D8ne1BxpVCdQ5heHTbmKB9ZoVoJm69hbvhHihZw7C0eeKy1VRIPXSqcRv5eaXoAzv7SkZlVezInSRNTIXYS8Mn6a6yczp9A8s9RD9EQ97VvSjvQ+NC5+L129h1JUUHO1ir6Trvccr/arr75a6lFWiW2ESSFJMEQdlAQfPny4qZwUHn5kT61Sgx7DCYhGCK365ZdfJD4+3iRAfvmVl+WySy87hlGr9lar5DhEMJr3EaBo4317whmRAAmQAAmQAAmQAAnUYALISfPPbzZKU/0h/8GvzoTAl5/RUJCzZtqaRNl5MNusbvTcPRKr1aUu7uQMacHJW3s1ko+nbZFnxq43OUkQNpWpOWm6t4o0iYgtLN/+vksrCPlLnibQHT19u/G+OK99PVm92xmaM2dtsjTQSkA//3lADh7KMSE2b/+6SwZ1rS8z1iZVyRysuRzN97XnNJYXv98gA15aIgM615cJmo8FdmPPxqLOQvL5/D3yh1ZGgk1aqfl6tNJTybApc/EY/qBi0u6kw/k+didkCvYkLNBHhvRodMSRW8cEy6/a64elBzTJc7C8NdUZtrRia5r8sGyf7V3UNsYpErwxebOkqcfVQU0YPFjHt8KRynvQBac3MGXTB/53uVzTI0amrjjg1v0nZbNihzOX0QoVCDH/SzVEqqEKYbA2xZW2ynof7unXxOREQkLsO8es1cpRdTTZ83Zzr/Wnou+k1d9bvuvUqeM2lchIp8eW28lqPkAS4xdffNFUc4KYNHjwYCk5r2qeAoevBQQo2tSCTeQSSIAESIAESIAESIAEvIcAEgNP1R/TBzXZMGxY/2Ymxw1+YD/11eGwJ7TbNAp3E22aaN6Wr/7RTR76dJ1MW7bXfDDG7Rc0dxNtzmwbLe9McSapRbjMqDs6SR0tO92rTaQM6BIjv6/arx47+6SJhmoh0fH4BbuNSBJT1/+Y54AKVp7CmDBPeBRFap4emI+P81/tfYu/zcniP3erWLBRqzd9vzBeUOkIa/j3De2NkIE8MU+OPcxpzEyn8OUq2lTFHF6dtE3W7XR6QmFaSCCNPUFSXog2ZUQhSVGxM8K9/ZrJTBXhXp/kDDs5r7Mz7wySEb+sXjgoyQ2DOHOL9oXQBqEK1kHPndIoRDbtzTTHJf8E+PmY0uZPaqLhNZqfaOnfSSY/UIemTiEiwN/ppfX895vtylYL1ycKPs2iu9mizZHeh+GaP+iT2zvK1W8tl8lamQqfK86Kdat+VtF3suQaeCxyyy23mGpVqOhEI4GjJeCjddpVy6aRAAmQAAmQAAmQAAmQAAkcK4G4u2fKqZqD5Zd/nmHCZSI1Oa2Vt6WyY+eoeLFXy2YjlwtymcBW7kqXi0b+Iai69M9LtEqUem3EqEdNSYEhT8Np0rMLTKJjiCDIbRKoJcdRTrsy5mkO17y10iQN9jTO04Pbyv/1b+rpksdzmGdCaq7EVrCEtzVIVc7BGvNov8HZVzcgJNDXMEcbrB0lUGMP9mk4WD3Nf4O+m9R7qLfmxPFkyEe09e3z7Etp+gwYSnzf/v4qGdInTt66oZ19/UiNirwPCZrfpk6w833FmvDelXyvPL0PR3r20V7/5JNP5Pbbbz/a23kfCdQKAnFxcUJPm1qxlVwECZAACZAACZAACZCAtxFoEOEUWo52XkgibCUo9jQGflAjH4wngziDylSwAB3HyqviqW955zzN4ZXr2sr+Qy083nZKgxCP58s6iXlWVrDBWFU5h7LmVtHzrnmBXNsl74d4hwpYliHh7w8juluHbt/Bumewgyqk/K2l3NNy8mS2lvD+bm68OX/dWUcO3zIdi/9U5H2whEHcUtY6PL0Prs+pqvZNN90kycnJ8sorr5ikwY8++mhVDc1xSKDGEaBoU+O2jBMmARIgARIgARIgARIggRNHAPlb8DmR5g1zONb1Q8Q5p7V73pWSY/64fL888/V6+zTCyJ65tr30dKkUZV+sBQ0k7R06dKgkJWm5+vR0KSwsPGGVnmoBTi6hlhCgaFNLNpLLIAESIAESIAESIAESOPEEZj57tgQHVV8Flg6xoTLzmbOlUSXDiU48Gc7gaAgM7FRfmkYHSpCfnwmDQx6ckiFLRzOut92zbNkyGTdunIwfP95UfIqJiRF/f39BSeywsDBvmy7nQwLHlQBz2hxX3HwYCZAACZAACZAACZAACZAACZAACEydOtUINTNnzpSoqChp0KCBtGvXTrZv3y5r1qwxpbK7du1KWCRw0hJATpvq+2eAkxYrF04CJEACJEACJEACJEACJFAegRQtfT1TS49bCXbL63u8rmEumBPmRqteAt9//71cfvnlctddd0nz5s2lY8eO5rtHjx6ye/duUxb7+uuvFwo21bsPHL1mEKBoUzP2ibMkARIgARIgARIgARIggRpPIDWrQC76zzJp9/BsuXHUcklMz622NS3YnCrP/bBZEtLyKvQMzAVzwtwu1DlSvKkQtkp1+uWXX+Tiiy+Whx9+WLp16yabNm2S1atXS2hoqPTq1Uv++OMPgVizatUqefnllys1NjuTQG0lQNGmtu4s10UCJEACJEACJEACJEACXkRAK17LeS8skpVbkmXIuU3kx0fP1OpYzoTGj36zUVAu/Zkf/rZnfNP7a6TX84vs48o25m9Mkg9nbJN4LbNdEcNcMCfMbZXOsc8LfwjmTKsaAqgAdccddxiPmuXLl8tzzz0nN9xwg8lZAyHnu+++k1mzZsno0aPlySefrJqHchQSqAUEmIi4Fmwil0ACJEACJEACJEACJEAC3k7g+yV7ZU9ypgzv10xevvYUt+mm5xRIXlGRfPX7bnn2ytaCEtWHsvMkLatiXjJugx3lgY+WUO/ZJtJ8QgId8tmsHTJ+8V6pbHnto3x8rb5t8uTJxqtmypQp0qVLF7NWhEc1btxYLrroInn88cdl/fr1JodNRkaG8bap1UC4OBKoBAGKNpWAxa4kQAIkQAIkQAIkQAIkQAJHR2C0CjKwRwe2LHOAzNx8mbB0nwzp0chjn4kr9svsDckSFuCQK7o1lO4tIux+8Ir5bN5uWbEtTU5vHiF5BYX2NatxQEOlPp+/R7YlZEirhiFyR984iQhyWJftb8wRos3o2bsp2thUjr4BgQYfyy699FJp27atXHbZZXLTTTfJrl27zKWJEyeac1Y/fpMACYhQtOFbQAIkQAIkQAIkQAIkQAIkUO0E9iRkSVyDMIkO9fwTJNDhK60ah8uHM3d5FG0e/HK9jJvvFH4w2dG/bpe3bj3N7jv0vVXy++oDZh0TFsZL3dAAtzVtT8yWPs8skBwXMefDGTtkxUvnSlgJ4QZzxFz3Jma5jcGDYydwySWXmHw2/fr1M4LN1q1bzaA7d+6U2bNny7///e9jfwhHIIFaRIA5bWrRZnIpJEACJEACJEACJEACJOCtBA4eypHGUYHlTu+u/nGybmeqbNqX6dZv28EsI9jUiwyS+f86V8Y+0M1cf2bcJvO9fm+GEWzaNI6Qv17vK4te6iW5+e6eNs9rUmIINv8bfprser+/PHH1KZKWkSsfzHZ6ebg9UA8wV8yZVnUELrzwQunZs6dYgg1Covz9/c0DPv74Y8H1Zs2aVd0DORIJ1AICFG1qwSZyCSRAAiRAAiRAAiRAAiTg7QT8kTTmCHbVmTESEuAn783a6dZzxfZUc3xl9xhp3TBY+p8aJe2aRBjRBRWprOtDzm4k9cL8NcFxkAzo2tBtjD81uTDGDg92yIw1CdKsXpC5vnZ3uls/HlQPAUuwQZUohET9+eefJgmx9bTffvtNrrnmGuuQ3yRAAsUEPPsmEg8JkAAJkAAJkAAJkAAJkAAJVCGBOmEBsj+5fM8VJCAe3KuxfPv7LmnXvI799KSMfNNuWs9ZbQoHjbTa04bdaZKcmSfJxddb1HcKMbjeov7hvjjOyisU5My5TcOoXC0rt8D10G5jrvXCy/cMsjuzUS6B/v37y1lnnSV9+/Y1gg1Ke9erV8++Z+XKleJwOExSYvskGyRAAoYARRu+CCRAAiRAAiRAAiRAAiRAAtVOACLLmu0pAs+YOurtUpbd26+pfK5JgFF2u6GGQ8Fa1Asx3ws3pcgdfZqY9srNyea7Sd1AaVIcdrV8e4YM7GxOS1KGe+WpOBV81mo4FMKr4K1TnmGO2/anS8fmkeV147UKEBgwYICceeaZAuEGHjZz5syRJk2ce2jdPmbMGBk0aJB1yG8SIAEXAhRtXGCwSQIkQAIkQAIkQAIkQAIkUD0Ebu7TWEaoaPPWjJ3y7BUtynxIMw1t6tKqrqxU0cay3u3qSmign0xbtldu1ZPxydnqXZMrZ7ePFj/1zjmvfT1B+NXo6VvF16dQ/DWpMbx1XO3eC5rKfR+vkUteXSIDz2go3bTC1L7UXBmmnj0IqXI1zBF2Y293ccG1D9tHJoCQqO7duwuEGwg206dPl5YtW5a6MTExUZ544olS53mCBEhAxJcQSIAESIAESIAESIAESIAESKC6CVx/luab0XCj96Zuluc0KbBrsmHVXdzsvgHuyWgDHD4y9qGupiLUVBVu4IVzarM68v7wU8198NwZdUcnKSwsknd+3irvTNkivTs2cBvz6jNi5IXr20m+hkl9M2eXPPr5WvnvxL9ljUtOG8wJc8McMdehPWPcxuBBxQmgShRCoizBZvLkydKhQwePA4wYMUIaNnTPQeSxI0+SwElIwKdI7SRcN5dMAiRAAiRAAiRAAiRAAiRwnAkkpOXJFa8vl637DpknL9YqT/CsqYwdTM+TID/fUmW6MQZ+2exLy5GYiEDJUwEnP79IQgJL/zt1cma+hmnlS1Sov0QUl/veoSXBezw5z0ylZUy4THykmzSIcPfAqcw8T+a+V1xxhSnr3bt3b+Nh891335mqUSczE66dBI6GQFxcnFC0ORpyvIcESIAESIAESIAESIAESOCoCWxNyJKlW1Pkgo71pW6Id2RsgJAzfXWCdG8VKS1LJDE+6oWehDfeeOON0r59eyPa3HHHHTJ27Fjp06fPSUiCSyaBYydA0ebYGXIEEiABEiABEiABEiABEiABEqgVBFJSUiQy8uiTL19//fVy2mmnmc///d//yfjx402IVK2Aw0WQwAkgANGmtK/gCZgIH0kCJEACJEACJEACJEACJEACJHBiCIwcOVK6de0qHTt2lDO6dJGJEydWeiKDBw82HjZt2rQRCDbIYYOcNjQSIIFjI0DR5tj48W4SIAESIAESIAESIAESIAESqLEE7r77bhn7xRfy4GWXybw335TzOnWS+++/X/756KMVXtPVV18tp556qsTGxpoqUKgSdfrpp1f4fnYkARIomwBz2pTNhldIgARIgARIgARIgARIgARIoNYSgGCzYc0aGfPww9LcpXrTyq1b5OH3P5Cw6Gj58quvyg2ZQtLhTir0REVFyWeffSYTfpwgrVu1rrXMuDASOJ4EmNPmeNLms0iABEiABEiABEiABEiABEjASwjcdeedsvGvv2TiM89IZFiYx1nd/c47snHvXvlRQ5085bq5/PLLTUhVYGCgTJs2TVAlCj8yaSRAAlVDgDltqoYjRyEBEiABEiABEiABEiABEiCBGkPgLq3qtGzx4nIFGyzmA81NE+brK1cOHCjJ+/e7rQ+CDZIO5+Xlydy5c2XSpEkUbNwI8YAEqoYAc9pUDUeOQgIkQAIkQAIkUMsIZP76q6R8+WUtW9XxW05RTo4kvvGGZM+Ze/weWo1P4vtQjXCPYugdidkyftk+efSbjXLJa8vkuyX75a/d6ZKYkX8Uo51ct9x9220y5/ffZfQ/Hi7Tw8aVyJePPSZF+flypYo0yTt3mktz5swxOWxQbWrjxo0yYcIEadCggettbJMACVQRAea0qSKQHIYESIAESIAESKB2Edh3332StXSptFiypHYtrApWk/T66yJBQRKljMqyrCVLJf7W4RLcvr001rK/Nd1q6/uQu3qNpIz9UiJvv0MCTmnjtduUlVson87bK/M3Jcn6nWmyLylT/H18pFWLaMlIz5GkQ7mSkZVj5n9Oh/pyT/+m0v/UKK9dz4ma2F3Dh8uc+fPlgwcfkr6dO1V4Ginp6TLo+edN/4maZPhg4kGZNHGSrFixQj799FNBeBSNBEig6gkgPMqv6ofliCRAAiRAAiRAAiRAArWZQPqs38QRES5SjmgTdMbpUv/RERLUpWNtRlHj15a7J17Spk6VcE0mK14q2rw0ZYuMX7hP9kKo8feTVnHRckanZnJKs2iJDj8sFhzKzJfEQ1mycuM+uXHUchnQrZHc2z9OerasU+P36VgXAI+Y+26/XZavWiXfPv2UdGnZqlJDIufNRBVtINzcpJWipsyaJY888kilxmBnEiCBoyNA0ebouPEuEiABEiABEiCB40mgsFAyfvpJctevlwD13PBv30GyFiwwPzR960ZK6uefixSJ+DeOlaAzuskh/REKC7/0MvGt4/zBlvLJJyK5ueKo30BC+/cX3OdqRYcOSdrkSVIYv0eC+vRxvWS3c//eLJlzfpfC1FQJ6NBBwi66SET/tb+ilrd9m6RPmy6Sn6tzu1Sy1MsB8wvt29ceIuuPhZK9bJk5Du55tq7nDPta2rhvxb95SykqKpDsPxZJSNduEtTrXBHNOWFZYXKSWX/h7nhxNI0zjHxCQs3lDP0X8vy9+xSUQyKuGSwZv/0m+du2SejAS8W/WVPTJ12TieZu3SqO0DAJ6dtbn9fCGtqEOuVo/8K0VCnIyJDUMcq92CKuGyI+6n2Tt2OnZM6ebZ2WotR0u201CjUHRubkKZK3ZbP4xMRI2MUXi1/9+uZyYXKKHNLcGMHnnCN569dJzratEjrgAglU3hWx/AMHJGPqNCMWBXbpat+S/ssvgmuRN99snyuvUaH3QeeX8ftcKcrOcu5Fn95uQxbq+5ahPPO2bBHfiAgJ6nq6frqZPjmrVkr2n6sk4tprxSckWBBOlvbNtxLY6TTTB55Kedu2iF9UtORsWC9hlw+SjJ9/Ej/dpzDdL8vKeyczNAQG72pgh1MlY+av4t+goYRefKE+L9ScP/TjRMnRNcDS9d3AWLCA9u0kuEcP0y4qzJeceQsla91aKUhNEV//AAnQdyLs6qvM9er+83+fr5PvF8ZLVJ0QubhXO+nctqGEB3n+CRMe4ifhIeFaBSlcBZ168tWUFfLr8r3yyT2d5dIuJ2/oDgQbhDbt1YTCRyPYWHtsCTc3vfqq3KP/Hb2vZcJpJEAC1U+A4VHVz5hPIAESIAESIAESOEYCCU89Jan6Q96y4FNPlay1a6Wp/ugMaN1KtvfqJfn6wySobVvx0xK16QsXmq4BTZtJ06k/i6jos+2ss6QgM9MaQqLuuF2iNEQAhh/Mu668SnJ37hBHSIjph3sLDibY4VHIzbL7vnvt+9Goo2JD/ddecztX1kHupr8l/sahZmw8Az/ii7KzJURFmQb/+5+5LXnUW5L40cduQzR45mmJGHKdObete3cJaNHCrN3q5DqHAv1RtmvwYMPCuh7QuInE/fijEQb2P3C/ZC5abOYQpXktkkaPtrpJcxVw/DQnxb577pH0efPs8yHKLeb99/XHur8kPP+c/vifZY/vF3lY+Go6ZbIKYVGSpffuf+IJcz/2JOKCC6SB5raxTfci/oYbJEur1liGcZqMGyd+jRsb4WDnlYMkWBOcuvZp+NzzEj74GuuWMr+LMrNke98+EnhqB4n9bIzph/3d0q2bsu4msS5CU1mDVOR9yFBPg70PPug2RNQtt0jUiBHmHMSn3UOHmnfKtVP9Rx6VOsOHSbJW5Un84ANpqTx9YxoK+m9VAa7urbdK9D/+Icn6wzhRcyr5q6iVt2+fOKLriWRlmr1r9tPPKqY1MyJaee/kgYcekkwVAI2YpmPAME6L2b9J3s5d5n0sUmEJ/13gnfQJCDB9Iq66SqJ0DrB9d90l6SqQwqw+QZ07S8y775pz1fln5OQt8s7PW6X3ma3k3C5xEhroqNTjCop85IPvl0hqSqaM/r8u0veUupW6v7Z0NmW9//xTxui76VrW+1jWd/eoUeKj/xv2soZKeqoqdSxj814SIIHDBFg96jALtkiABEiABEiABLyUQN72HUawCenRXVrqj8cWmiA4PzHx8GzV06W55miAeJGtCTF969aVVn/8IVHDhpkfzKaveqIgN03rNWuM0AMhIunjTyRfQ0NgGfojGIJNlOZ7aL5ooTQZM6bUj+0D+iMa4gKEolbqBVFn0CBJhVdKsXfC4Ql5bqWM/sT8OG701lvmGXWuu84WP3AHBBcINphbc03yiR/zEKESX3cRPLQfxKrGoz+VVsuXm76YA0QGWPJHH5oxY998U1qtXikQfHLjd0vaxAnmesNRb0v0I84f41laOQY//pu8+565lq3jwSDQtFZBBc+HCJG5aJFk/qzCl1r9518wrCFoQVQBd+sDwQYWrAKadc6cKPEnfdpUI8ZEaDUa7Gc9TXIKcSf5o4/cehaoiNHs56nSct5cIxYc+mWq2/WyDuC1Enp+P8lcukwKkpzvSaa+D7DwSy8v6za38xV5HxL/4xTr8K40Vy8V7FWSenwV7N1jxkr+6AP7ncI6sad4h/2inZzcHljOQRMVbiC8hZ17jtR/8knTM3fbFvNdkXcSbCPVq6ql5h6JvvtuKdBcJNkr/zSiD/ap/r/+ZcaK0XfG2jdLsIFnEgQbiKR45/HfEPocD8Hm4zm7jWBz2ikxcmGP5pUWbLAoh0+R3Df4THH4+cn9H6+R5dvSzFqP15+EhAQZOXKkTNf3I1sF2uNt/8/edYBHVS7R2Wx6LwTSgFAUEFGkiCBIFwURFBRRVEBRwGcX2xPBp4IFBSw0RUARKQooSFfp0nvvLSSk9767b86/ezebShKSkJCZ79vde//+n3sJe8/OnIGHzRNMHB7nkCiENpUVYYN9THvpJTIlJtKTTz55TdvKZNLwFHvcpaWlXdM45dk5gb3VPvnkE0pmXZ+qaLgPv/vuO/r444/Z2TSzwC0cZS/WSfzjAV5n2NMyr+FeiuS/B2LXB4EcX9rrM7/MKggIAoKAICAICAKCQJEIZJ04ruo9ez6gQon0gYHk3r1boX08B3C4iYcHefHDSuDX/Gswe4jAEO6CcKDUtWvIgceAZZ05qz4zjh5Wnx79+pPOzl6FJOEhXDMThwKB1HHmh1dDdCSl799LjvXrq+pMfuAojmUcOaK8HNy6dlVzePJctgbCCYY5sOcMfjB3bNhQET0a+aDqeV0u/PCvY+FPt/YdVB94TcDSOdwKnhQ6dzdK376DHAOCVHn2qfxrdO/UWT24O/FYwMmJQ85gCOlBeFYiezbpPTxVGcKlysqyDh9RQ3kNHKCup9cTA9V5ho3nDQo8uvD6OBQIZJALe8lkHDyk2hXnzb2vmZxJYYIPlrpunfp0K+K+UQ0sb8W6H8IuKdIM4WvwEHK/t4fqnX78hPlzz1716TNiuNonPMCCmGxz4zCVkpgda4nYcdgZrqlO82wymqgk96THwMfIjr1oXLt0UVNnHDtWrCXY16ihPH1AFEb9911CeB7IxfK2w2EpNHreUQoN9qVHu996zdMN6duSktMMNGpe8fZ9zRNaBvDnkL+4uDh6hz3P7mQvuZEjR9I6y71YVnMUNs6+ffuoG4eBHmOiesn77xcrS1RhYxVWDuKG+O/qCNbKKY3NnDmTGjRoQD3v70k333wzzZ07tzTDlEmfI/z3uX379gWOFcFeagvYExDkR1UzEC0tWrSg3UzKO/PfEQfL/4eF7eN7DiM+ccL8NwxtQFg9zp6RzZo1o5b8d3gw/xgiVvEI2Ff8lDKjICAICAKCgCAgCAgCxUfAmGT+dVMfFGDtpA80kxHWApsDp8Zm8gHkjpuFnIGWS7hFNNOJw4sM/CAFM2Znq0+DNgdr4mhmj7CUi2YyxGSph9eBFiqiteOf0K2HRR1kJyaRU0iwtQk0dRBuohlIJZgKWeKHGVtDGJVmWJdmJrvcejrG5BTlSRE27Dmtifo0ZmTmOseJ461NVRlCZ9w6mx/ms8PCOEzsIUUUwbvDZDDjo7PglG+QUhRkW/ZpX8u8D5BkCAEy8MOBreks106V2Wj22LYp7Nj1zjaKvEpeu06FVCWvWUPurFOk6RsV1k8rv9r9YGQPAxjWrZmeQ5yUWeoM8QnKM0vn7KI1KfITujglseLekyoUz6LrxE5pJTPGPej77yj+h1mUzOGBiYoE+4hqMoHjOfDxko1VgtZLdkeQq7MD9enURHnLlKBrgU1r+jhTn25Naf7K/fT30Tjq0qTiwqS+4PChVA4/W7ZsGa1l/J577jm67bbbqC976j3G3nZ4kC5rA2Hz2IABlIJ5P/qwXAgbbc1IB34XkzfD2StvGrTFimkgScaOHUsTJkyghzkcbz97A9WqZfk3xGPs5Mx9Wax91bZtW5YN0ylvnIz0DIqOjlYzdGCPPjvL3wW0+4vDFUEA3XTTTap+FWtYwdD/H9bYasMaTYH8N+X8+fO0i0npYCZaUYaxQVKsX79e1a2w6KG1QthqzZpqHSBr4KWC/rZ28uRJOssaX126diF7vT3BG+XAgQNUg8nOU0yUgzRztfkbb9sXx0iVnsFekrgfLl++TOfOnaN27dqpZjhGZq4U/sGgTp061JH/fsFAomCt2L+vr9lrr7C9ov1a1rJq3bo1TeNQTFg2/01fs3oNdWECF/cexrqVvSabsFYcXr/++qtqp72B5EH9VPbABHmFPV3gtO9Yk1jFISCkTcVhLTMJAoKAICAICAKCQCkQwK/9sMyjxwjCvDCj5cFfneR5Q3hMXov5ciIhpCfk96VKmyWVH+IvWzQ70BZaLrBsnsPxNnO2I2OKmSxCuV0tcz0In5DfWR+GiYaSmiM/1Kfygwn0dSAcjJAmW40dx7qhakhv1m2pwfotpTFH9kxBOBTCdeD9UZTpPc0CzbZtEKKENdVbs5r0QcGssxLLOiv32DZRx3o3VyVWDJHakmJhHxSkxkjbf4DcmXyDFxE0W1xbt8o3T6kLGF+v3g9QLIcupaxZp/bk3rt3sYe72v2gtzxcplm8aTBw5v59any9ZX+4FtBWQiiSJj5suwB4z8CM8bFK0yavp5Ft24KOy+KexLj2LmbiMIPFkiH+nNcgRI0QKn+uyGaPqzAmHWI5pK48SZvfd0ZSQxYSBtlSVtasQQ3aEeJLv++KqFDSBuvHg/sAJlHwio2NpcWLFysSZ/To0eoh+D//+Y/yYiirvQ6FNwT/nXnz0QElzhJV0jVAnPhd9sT4L6f93seho81Zj6o4BrIgJCREYYL2IEk0A4kTYwmBhbcSiITly5bTHCaFApgovchk+iOs3TVmzBgVsgMCAoTNab6Hh7Im1Gv8tx1Exm+//abIDYQEvfnmm3Tk6BHV5o8//iCEA3kxmQkiDeTLxo0b1fR/WkJBQeqAtNnK/4YPsRcgxtvOIaVBln/f48ePp1mzZiki57333lP9Mc6z7HUEckev19O3rPuE0LjCbNGiRcoTC8TeJtYC+5GFnTH/9h3bqT97YoJUApEFbxmQNiCmRrDmWPPmzWkU6xPBUwnkTUF7BQl25coV2rplq1oPSCDchx7shfo861RpewEun7Mum0YK5V0r+rxrCct84403FCmGayBWsQiU/BtHxa5PZhMEBAFBQBAQBASBao6AE4vHwlsgjh8KyE5PJr0dZ9n5xYpKNv8Kms66K5kWr5hk/kVbz1+2tew3aGjPWZSQeSqdf3E18sNMLGu7wFIt4ruu/IU4lr+AR/JDFLILZV2+xAKuu3M8YfjXWOi7QLMk4qnB5NqtO8GzwsjZdDSRYDVgEW+unTopPZqIF18kjx49KNXykKB1gecLtEPiF/1KJvaYcbmLf2Hm9MbEYp+22aW09gV9evDDE4iCy0OfIfee95FT4yas/xNNHpxtyI6/rENPJuPAQdU1Zctmxux8rkxEDrVrq7pkxskppA7F/vSjOkfmIEd+yNLWgQxeaYxn1GtvkCuTOkbOhuXesbMiH9I4LMsQeUX1w1sWe+/gmoCkcO3enTzu60Ex/DATzVofmceP8TVYr9p6cPhbWZqHhbTBPLh/tLUXZ46r3g9MCnn27KlSZUdw2nMHvhfiFi5SnjeOLNIL8+SHSlyL8OEjyPPB3uR0e3MyJCWSK4cYODRqzKFtd1MU60dEslaGa7v2lGx5WMw4eIAQSndVK4N7EnM43NRATRXHoVss2kEO/CCtc3FR1wqZpxLmzcsJJ+QQQaso8lUXWLoGm07G08XIZGrbwryu0o1ScK/gAB9as/8S0ZNNCm5QAaXwjsCDPV7wtFi+fLkiGaAjAiIChEVhYTrFXR48bNz5Gr7Yt09xu1xTO083M1G+cdcOat6rJ7vxOV51PBAKCLmBffDBB0ovBgTELU1vUZ4vmzdvVnUIK0PWK5gn/y1cyRpeS5cupYmswQTSZg0T8AjbAUECLxekIQdpg3qQNt35b87T/Lf7ZRYNj4mOUR4m8DwB3tD72cb/d/RifS0QSMAf49ga+kGPSPPgQZ3JZKKff/5Z6cTcc889ilwB6ePDemrQ5gHJhDZYFzyD4HlTErvI4a4ufP1AQMHzRhN6xn5eYXHxp/j/KGjU/M4hrMCsoL3Cmwj4XLrE9zsbSCqQQO9zqFxpDKFrG1iXCy/HYlzf0swhfQpHgL8JiAkCgoAgIAgIAoKAIFB5EdC5uVHg5xMofNQbFDXhc/UA7sQu8BBYhV5NFru2R1iyFWEXOIbXhi1p48u6ItEfj1PeNXiAhxhv5IcfUQL/4q3nX1t9+Yu+Hz98g0yIGPO+8spBOE0au+hr5gP9Bv71NGH+fErh8APNPB95lLONG8lw/oJWlOvThPAf9rrwfnowZfFDWiJrWiRDlJbd9pVpX4CZCAic/BVFfzJeCRxDYBgGYeJCCQd+cLc1tAtgkiJq4iQltKzVOTVuzOFQzSjirbe1IrVXnDRkQkcz6OykcsrxqPGfqCKILWN+iBGb0tKt6/Ab+QKHYcWovWA/MKSmdg3oTnEcTqMJ/6IceihpfE0g4hzKD1D29epT4LhxFMnrhBg0zI/FTN37Fe8XeoSRIatXgcZpyjUvGRAjzg0aUvrpU+TJqd8RBqYZ0pLr2EuoINPztYJOzdXuhxrvvM1eMvHqWmIceHLV4vsTWbZgIKggBB379TcUN38BEV5sEF725rXZM6Hm/cTjhLTbIAiRHSv9g7FKQNmZrz3CNpTxfaFMO8eJJSyuqHtSpYG37aO6mce0js1l8KjC3LHTp1H0lCkYXeGG9WddvGS9T1QFv8HbzJ8fBsvL1h2IJgde960N4NtjKtNpGgT70KZdp0scIgWvkNKawWCgbA4vxAthPNqxdg5vj4EDB1rDduAFgpAUhK7s5YxPpbF3OdveeP73Fc8kHDxhyttmrlilpujDfwuLQ9igMbw1sFdYaGio8kiB7goIAYQA3c/C8jA/1oLSiAfo3sA0cgTH8G75m8l3kDOaAWd7Fp+GQc8FYVRff20m6kHqwKsF4T3wwAljUlmz4oohIwwKa8S68W8JYyEcCmQQyBZ4B2mCv8Ud01ao+kHWvcKeJ7NoPbxioIkEPSTNI+gnFieH3W4hiLX1590rcEFfYPHhhx+qZnnXg9C94hhEmOEZljdErDh9pc21IyApv68dQxlBEBAEBAFBQBAQBCoCAf7l0sDhOnoWpg3jhxykg0YGJQjyFtfgOQCPExWexFoBJn44Ug/Z/MUbhnAfU0KiEr9VGZkc9AWG/yBsyJSeQTr+BRX9QQKcxy/MBZhKsbxhvbXGlJqijtP37qMw/kKO1Nu+r75qrVcH7A1kjIwiI3sVQcBWPYDnbnHVM5AbRv6ibeflyem+3a7a3rYB0mYTzw1srccgIzQCwdLYPEcSEyKcwpw1ekpkfD2zOVRE7+NVIMaFjZX822JFrBVUj4xUNTnL19XsbMdOSvunoHZaOm3UFed+AD6mrMwi9XKQztuUnkp27h5KJNt2Xi3zl8K6gHvStm1Rx3nvyaLaFlhnuedMJiPp+J6DcDEM+4NHGczOy1uljlcn5fT22i/HacuJJHru4RblMsN/v/mL5r3SkkOkzHogV5sEXhOv5v33ebVOZVSPVN3/ZQKmNDac/7Yga9Q/HPpSnraewxyf/PQTeqxfP/qcPceKa8dYDBtEC0Ju4AnzA3tSggh5++23FRnxFacU78qi7TCQWMhqdJgJYHiYwNsD4UE7OJsZPGXgtQQvlEYs0g7CBu3h6QIyBW3r16+vxgFBgTYIM0LYUScmuTEfwtZAxMDzByFLdzFRDe0X6NTANE8bLaQIZVg7PHS0F/qBqIH3DjRycIyQLYRXIWVzQTZ9+nTlaQUvFnj0wPsInkQgVkC0IMQKxA3CpkDgwaMGawA2CN0qaq/afHlJG+zrFhadxxjwAHqINcxmz55txRo6S3dzmCTuPYyvafJgH278A0peokibRz7LDwHcP+JpU374ysiCgCAgCAgCgoAgUEYIIK22MSmBDFHRlMS/zoKwgSdMSQgbLMVWiBYePGaqJmeR0GfRMSkEK2psLb211lMfFEi1LZ4UWpn2acfEDwyEUTaTOwb+Qp6+ZzfFW9q7cahUPmNyxI5Dbiw+Fvmqi1OADFp6EFSlMFtdINvjvENdyxz8E7WZkMo76FXOkQGpNj94FWR63+IRRyGcjtuYZShoCPY8yREbLc79AHx05FLgWFqhmdAqeG2291lB96Q2xtU+896TV2ufr95yz+Utx/70BehE5W1XVucp6QV7QJXF+CkZBV/zosbu3581pkoY3lLUeHnrIMiLB3GIB8MjBCEsnTt3VqQCPDZKa9OYEOj9wAM0gr0Hp7IXYXnZF78uottZqLYkhA3W0pi9/z5hsmf8uPFKjBhlCGuCp8o49sQDWTFs2DAUK+FbdVDAG/BCdiOkHkcYFQgHeJV04b8TMGi1fMReRyCGQECAVHmAcUHIEQgckEah9UKpzZ1taMiQISpECv1AdmB+jfBBGebCuNDWeYs95rDOKeydBtIHYsIISSqJ9WadrXkcfgih3/vuu09p54CYgX355ZfqE54tIFBgWDuElBE+B2IHxBU8kgraq+pQwBuIKOjiYG+33HIL1a1blwYPHqx0gtAce8HePvvsM4UBPHxg33zzjfp3ALJHrOIREE+bisdcZhQEBAFBQBAQBASBEiJwnjNWQEtDM3eO46/BOghaKIxWXpk/k1h/4IrNr+YI0/LncCX3fg9X5mXL2gSBCkXgiSkH6HR0Fg176I4ynzcqPoMmzd1cIk+bMl8EDwjvEHh7QAcForrQLYFXCcKkytLgPfIQh9o0ZiJy6gv/Kcuh1VjD+QH+OHuHLGEiXdNdKc0kiZxxDWQNPGRsDZ43CBNzKoY3JTxrsF93DgfLO47tmDiG5wzGRIgazLY9vHGQ0Qlha1p2KtWokDd41Fyrxgvm09vryWgwqrUg5Ap7gacNhIPzGjxgEK6EMLHSGuYEBtrcmlcRxsP4wMh2bpTpOCzTtl1p55Z+JUMAnjZC2pQMM2ktCAgCgoAgIAgIAtcBgYx9e8iUbSQ7zlrkULdeuYdolMcWs1kDJuvUabJzsic7bz+lcwNvEzFBQBDIQaDvl7spMsVEz/RtkVNYRkfnryTRjEU7rgtpAyFYhPBAT8WWqOnBnnZa6uYy2mauYfDw35c9S5qwx8ZU6HKVgUEr5yEmzY0cQvf7NRI2ZbAcGUIQuKERkPCoG/ryyuYEAUFAEBAEBIEbBwGn5mX/AFfR6ECbRunTVPTEMp8gUIUQqOvvSuej48plxVHxaeTv5VJsPZuyWARIGoT6IAQKYS3wpoEOyrWEPpVkXfCAgSfMQ5ztbAR7xlwrcbPvzGl6jEXdW3Da6SkzZlyTh01J9iFtBYHqjMC1hEpXZ9xk74KAICAICAKCgCAgCBSJAER603bsZCFXs/BwkY2lstohgPtC3R98n4jlINCnVS0Kj0mhiFgWwy5j23P4EvVsUbOMRy16OGiWQHMEmXx++eUXpR9SUYSNtjIfTjO+lImbo5yRCMRNaU0jbDqygO+8hQuFsCktkNJPECghAhIeVULApLkgIAgIAoKAICAICAJFIWDiDEBX3nzTmgq63to1pA8MytcFWYNsRWjzNSiDAmQUSlz0G7m0v5ucWHTS1hIXzOc03qzdMPhp2+JiH2dzqtz4n34kbxauRBrvirKMAwcobdt263TunLXLPjjYel5eB6lr11Im6yp5s+BpWZgh/DKd7X6vGgqi2rU++SRfZqmymKcqjtHmva3UpFEAdWgeWmbLPxWeSLN+20kr3r2LWtTNrxNSZhNV4oHiOFvbQyx+2yQoqMQeNwiJ6vruu9SaxXinTZtWiXcpSxMEbiwEEB4lnjY31jWV3QgCgoAgIAgIAoLA9USA0yZf5kwcyZxm1pczkdSZ9wvpA3KyEcH7Jmr0aELK6dMtW9I5DpeIGjuGtLTPZb30bCYZor+aTOn79uYa2siikpEffkRREz4nkEy2hvNIkE6rVtkW5ztOZmHl+Lk/U+Kvv+WrK8+CjKNHKGHhAor7/ju1t6wzZ0o8XTyn0D17550FvrIvhxU4XuLSpRT39dcF1pWmEPcF7g/cJ7hfwvi+Ib5/xIi6Nfen0+eiyxSKPYcv091Na1ZbwgZgKo+bP/+ko5xlqSQeN0rDZvx4CgoJsRI2u3fvpoXsbSMmCAgC5Y+ApPwuf4xlBkFAEBAEBAFBQBCoJgikckaYNBYcrcFpUr0tqVK1rYOYudj/EcoMu0SerC/h2KwZpXMWmQQmPdzv70Uube7Umpb7px1naQn84gvi1CGENNO2ZsxIp8QVK8iehUs5D61tVa5j90ceIbJ3II/eD+QqL+8TzwGPEV7wfLn86qulmy4tnQycJcZ70BOkc3TKNYadS248clWW5QmLUDve1ox8+WXHmXOiOXUw7h/X7t3LcpYqOdZjdwXSit2RtHnfOWpfBt42u45H0oWwaJr/StXXxrrWCwqNm6XLl1Nf/ndbXI2bQZMmkZHv159++klNjzTXq5jU3bt3LyUxEf3MM89c67KkvyAgCBSBgJA2RYAjVYKAICAICAKCgCAgCJQEgcTFi1Vzj0GD8nVL/GW+Imz8X3+DvIYMNtdzqE3myVPkeFNDa/v0DRspdc9u0jm7kFune8ixSU5YE0KaHELrk8lkoPR/t5Fri5bk3KE9cW5aa/+07TsobesW0vv4kmOjm6zlODByetok9u7QTOeSm7DA+NnRMao6/cB+Spg9Rx3rvb3IvW9fdQwdlswjR9SxjrPHGBMTOPwrx5sIFdlRUZS8ciWZ2NPHoUFDcn2wN4EoUnXHjlLKth3k3qM7JbPOhyklTRE/tmOgbyZ70Ojd3MmVMXAIraf6lvWb34sv5SOttDngFZX4x+9kDLtMzhy+lM/YKyaFH34zmaRzbNKEHPg6pW3ZQh59+pCdj7dqjvC0JCbAjJfCSF+ntqrTueYnhXC/gLRJ/PVXIW0YuWYh7vTN0KY05Jt95OTgSK2b5g8vzHc9CimIS86kv7adoM8eb0S387hipLRoli5bTn04HfjViJvh7F2WxOmff2fPOi2t90gmpOFpAxs7dqwibl555RWBVhAQBMoJASFtyglYGVYQEAQEAUFAEBAEqh8CWSz06Xr77aT39My3+ZQtm0nv6koe7N1ha7aETSx7v8TOmmWtjvn2GwrkzDNuXbuqspgvviTHevUo7fBhdR47cyZ53X8/+X/+uTpPXrKEIjj8SjPHOnW1Q/WpY88ahBXBsjkVsD3/6g6vFc0SONzJEGfO3JO6c5cilFDnFBpqJW3St23l8KRFZGICCN4qNUe/Rw6NGmtDEMKLLj06QI2vFbosWkhB8+aSzs6eUvbsUWFZyatWUtqhQ6oJQrhCWSjVvr5ZGyeZj5M5644yDuFyvesuCpg61Ur8aOOW16fyimJcMi+cV9fMwOFUebFEmFsCP8hq5tK0qbouLnffTY5M2hg4BOUieyMBZ83iZ/9Itfka6VxdtCL1ifsF903W5cu5yqvzyd03edOkobfQ0Cn7KSXTQHffXocc7EwlgiQ6KYNWbjpJwzrXpb4tapWo743eGAQM0nX3YZKxIOIGIVEjv/2WwhMTC0zrPZP/9owYMYKWM3H5Bf/dgsfNaJu/PTc6frI/QaAiEcj5WaYiZ5W5BAFBQBAQBAQBQUAQuAERyDh7luxrFfxwmH3hIjkyKaF5nOTdPoRpQdg4s2dK6OrVFDJ7tmoS85mZkNHag7AJnvkDNeBfukFmJMCjhckYWNSkyYqICV32BzXgFMNODc0kiNZX5+FBoZs3q5d7hw5asfWz9rJlVJu9S2C+HPKgtQ2cO9faxuell1V5kGV91grLQdz0GYqoqPHWW1SfPU88Ob0xyJmUlbk1cvScAh312AssafUaywikCJqG3Kf+ur/I9+mnKZXDyFJZi6OsLZy9AyL4wROvyLffsQ6fsvxPRdhAbyaUSSpcCxA4mmWdO68IG1cOacMe6nGoVnaM2UNJaxM3Y7rCIWjiRGpwYJ8itxAal7jU7I2ltdM+cd/g/hHLQaDn7TVp3iutaPf+CzRxzmb6e/cF9vrIzmlQxNH2w+E0Y+F2auCnp1G9cpOXRXSrVlWKuGHi8diVK/TjunXWvf9z+IgSHU7ikCikC9c8bKwNLAdTmUh96KGH1NkMTv/99ttv520i54KAIFAGCIinTRmAKEMIAoKAICAICAKCgCAABOBJU5iZDPywaSrcUyD9+AnV1Z31bpANCS+QMiAsIA6sac84N2pk1b9xa99B1WcxIWRfw5cMMdGKJNGyObn36UtJf/9T2JLKpTzD4j3j9cRA5VnjNXAAJTLhksUPgtQrR//Go38/svPyUnsBblnHj1nXgzCuTM4Slb57D+k9zF5LCJcqa0MIlMne/HXYaBNilnHU7Mnk0a+/2oNzq1YE3LMuXlRLyDpxXH169nxA7YF4H+7du1HcTznkVvqBg6T3q0E6dzdK55A1xwBziE/2qVNlvY0berwuTXxo7X9b0zd/X6bF/56nHfsvUmhtHwr096SGwd4U7J8T8pSeaaQTF2Lo6NkoOnEuikb0CKW3epVPaN2NAjoImYmcBnzAo4/S6v37+N+DA21iwnfgwIH02WefXXWbX3FfRw6TXLBgAf3888/K4+Zb9tAREwQEgbJDQEibssNSRhIEBAFBQBAQBASBao4AvCUMkZEFouBQtw5lHDxEJmO2IgLyNeIwBJg+IMdTxyEgQJUZuU5vEQy2t5ShwmSnU/V4MyQkqWP7oBx9GQebY2vDcj4wJCQQ1o1QKJh9LfMespkgsTVtb7ZlOEYq8Yv86z1CrxyDQ0iRXVyuyy6eh0Xe8Yo6D+IQD40Ms21nSEpWp/rgHC0V4K6RNkatPsi8NzTOm9bdmJyiSLSwYc/ZDk3GjMxc59oJ7hsnDn0Ty49AsK8zje9fn/7TJYg+XxNGWw5H0aETEbSGSVBXZweq6edBowa2oNembqAMg5E6Nw+g399sLRo2+aEssKR58+a0fccO+uabb+gI61XNZc+6jgXpOBXYm2jChAmKuIFQ8R8IbeTQqjlz5hTSWooFAUGgpAgIaVNSxKS9ICAICAKCgCAgCAgChSDgEBREyRwuY+sZozV14QejVPa4SPj5F/JmAeK8pue+sEz+tZuFJtRx2h5zqm59ISFXqpHlzb5mTXWUCY8WixmSUrTDYn/aOTurtpml9AhxqB1C0MMxxMaQ3teP0vYfUOPZW/Z3tYXEcZgFCJt6a1aTPiiYIOZ7psM9+bqZLMLGBn5ALGvTsMw+ekxleML4xpSceexr1FBTZnK9S9t26tiYh5RyZJIO4VAIdYPXVFGG+yVl3z5yZz0cscIRAHkz6bEG3KABpWYY6FhEKh0OS6LYpCzqd5OObn2vLdX0ciQfN3nEKRzFgmvgcfPee+8VXFmM0nHjxiniBlo3f7PAODx1fvklR/S8GENIE0FAECgEAfmLVggwUiwICAKCgCAgCAgCgkBJEXBnDxGQNokcKuA1dGiu7t6Dh1DCgoUU/emnlHWMH/Y585MxM50yDh2mGqwF4chCtPA+iWORX2ItiayIK0pHBULDttmhcg1qcwJxW2isJG/dStGfjCfnmxqxYPACmxZMCHGmqkzO3gTLZs8OiAkns44NzK1rFxbIdVMveLgkb9igxnG5pSkZmLDwHPi4aofMTsReLxBdhmVwGFAy93OoHUxOzVuQR6/eirQJH/IMuXbpRImLflXtPO7roT6v9uZQu7ZqkvzPP+QUUodif/pRnacdOUyO69dzRq1O6tyJSRFY/PffM0mWyiRRLIcodSUtNExVEn5TNgAAQABJREFUXuUtgb0BkAHL1jwHDCBX9jKAvlAkC6t6P/UUCwRfotRdu63hb06tWqrjuB9Yj8dOTya9HSXmeUD1ePxxdS0uD32G3HveR06Nm7DuTTTjYwmpspkU9wtMy9BlUyWHhSDg6qSnFnU91Etr0iio8PBErY18lh8CyCTlzKQvwqM2s3ZW//796VfOiCYmCAgC14aAkDbXhp/0FgQEAUFAEBAEBAFBwIqA+733qhCXqC+/JFO2gdx69CCERcEgAlx70SKK+ugjJWKrZR6C8DAyLiEDUwDrQ1x5YxTFMbkDc2/XjvzezRHIVYW2b0zu2Jr/6DF05eWXKZ6zQMG8n3jcmmkK56lMhCBTk61FvGMev+7yZdbU2jU//ohiOCMVxtFyH7nf11Olso4YNcq2u3UvKosVkzbuD/eh7DOnKYYzLqWfPqXIjUD+FV4jU3R51mwdzFLuyToyqf9upajxn6gqL041btX2SUu3kjYYz5cFhEGWXBk7RrV1rBdqncc6bkEHlrmQZjuvuXa/l6Bh4/fCCxTDD58RY95XmaPcmchJ27lTNUdIVeDnEyh81BsqExY0eZxuukkJD+ssHkAglwL4WkdNnESx331vncapcWNyZsIOlnX+AqWwJw6uCUKj3Pl+ERMEqjICECP2Y5Hx//3vf7SdtXEe5LTiCJkSEwQEgdIjoDOxlb679BQEBAFBQBAQBAQBQUAQsEXAGBdPEa++rDwzUI7MQvrAHJ0Z1Za/fmWzZ4gdp3ouKJuUkXVhdA6O+VJD285T1DHWQCyAi7FNqWmkc+GQp8LIkiIGQkYkHXvj2Hl5Kg+cIprmq4J2jyEugez5Aa40hnUTe7DonJzUHtQxCBEbwWCMi3lMkbxOTrONtmVpauyERCarfM0Zuhz0Vq0eNQ9fRwOHb+m5PozDQZAlC1m98q4DgsdGDuOyxREpwc92766GcWXPnQDOMoV5xASBGwGBxYsX08tMIMNatmxJS5cuvRG2JXsQBCocgdrsfSqkTYXDLhMKAoKAICAICAKCQHVAwMDeMxmsL+PCGaDgZSN2YyGAUDNjUgIZoqIpCeKrHE4Gb5yAYmbOAZGTxpnBnJreorR7bix0ZDeCANF6Dmd80qLf1aZNGwmVkptCECgFAkLalAI06SIICAKCgCAgCAgCgoAgIAic79aNdYcirEC4d+hANT74gDQRY2uFHAgC1RiBw4cP03333acQaNu2LS1caA79tIXkIw4jPHr0qEoZblsux4KAIEAkpI3cBYKAICAICAKCgCAgCAgCgkApEMjYt4d1i4xk5+bKukX1Sh3KVoqppYsgUKUQCOdQwDvvvFOtuQOTm/Pmzcu1/rS0NLqdhdhXrFxBDVnjS0wQEARyEABpY5dzKkeCgCAgCAgCZYEAMsHEz/iO09Rq8p1lMap5jDROFxw7YQLrOxSdxrc811B2u5GRBAFBQBCougggUxYEix2b3CKETdW9jLLyCkAgkDW9zpw5Q3q9njZt2mQNmdKmdnFxoaGcbW/smLFakXwKAoKADQJC2tiAIYeCgCAgCJQFAml7dqtMINnR0WUxXK4x4qZNodjZsyl1hzmDSa5Km5PyWIMpJYUi33yTkletsplJDgtCIPaLLyi2mLoWBfWXspIhsOVkPPWbvI/2nksqWUdpLQgIAoKAIFAhCDiwiPi5c+fI19dXad0MGTIk17yPPfaYyja1a9euXOVyIggIAqy/LyAIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIVD4EhLSpfNdEViQICAKCQKEI+L0+ivzfGEWuLOZX0WbMSKfEFSsok8UCxYpGIPmvvymNXcDFKgaBM1GptOVIFIXFc4poMUFAEBAEBIFKi8D+/fspNDSU1q1bR88++6x1nSi7//77aZV481oxkQNBQEPAXjuQT0FAEBAEBIFSImA0Usry5YrMcGzShHQGY76BjHGxlMSEh/FSGOnr1CaPPn1YA8EtVzs85GccPMjCllnk2KwZubTvQHbsTgxL4XSy2bFx1vam9HTSOTlZz6mM1pAzYO6jxAXzKTs6RhWmH9hPCbPnqGO9txe59+1rbZx59AilrN9IpvQ0cm3Rkpw73mOtK85B1rmzlLxyNVF2Jnk88AClHThIdl5e5Napk7V72r9bKd3iPu3Stp3SlNAqsU6H0PpkMhko/d9t5jV0aM9+pTm/URR1LVJWr6bscM4G46Anz/6PUMrff1P22bPk1usBFhqto6ZJXrmSMhGb7+ZOrp3u4fnqadNT+oaNlMHtjYmcBpjDyTSc0MDzsQGkc3ZWbYtag3Wwcj4wmIhmbbpEe84m0h2hnnRnA2/6c18UDesUQv7u5vsuMjGL5my+rFZyNiqFGtRy5fra5OmsV2W7ue/qwzE08K4A+nVHJCWnZdFQ7l/Xz7xPbQt/7o+kDUfM92+P5v7UtYmvqroQm05zt4ar486NfMjdxZ7mbb1MTUPc6dE2geSo19GVhAz6YsU5srfXqfL+rQLIycF8PaOTs2jGet7D6QQ1xoJ/r9CBS2a9pzvqeNL9t/lpS6DC1mBtIAeCgCAgCAgCFYIAdG169+5Nq/n/3OHDh9O0adPUvI8++igNHDiQnn/+efL396+QtcgkgkBVQEBnYqsKC5U1CgKCgCBQWRGI+u9/KeH3363Lc6xTlzIvnKc6S5aS400NycBZEy4+8ghlx+cIEzsGh1DtJUus4pWRr71GiWvWWMfAgXvHjlTr68mks7Ony0MGU+bJU9YxQpnEsa9f39q+LNZgHayAg4v85coQF2ed397bW7Vy4l/GAufOVccpf/1F4S+/nKu379NPk++oUbnKCjvJPHGSwgY9QYbUVNK7upKdpyeTP+nkykKfNSdNUt3ivppMMSzybGs1R79HngMeU0VnOTuFY716lMYpRjXz4l/u/D//XJ1e7VpceelFSt22Xa3B95lnKHbmTG0YCmUCB6l8I0aMoGQbLxrXu+6igKlTFcEWNXYMpaz7Kx9OGKTOsj/Izse3WPeDddJyPHjs2/20/kCkdYYQPxe6FJNGq95rS81ru9O5mHTqOHoLZeQhIT3dHGnPuPbkzsTNhJXnaMLSkxTi70aXmNTRbO3ottSMiRfYG78cp7nrL2hV6vOdfjfTy/fWpY0n4ujJSXvUHPe3CqR1uyMoy/K15L6WgTT7uVtVm0e/yNE4cNDpaMKQpjSASZ2j4Sl034fbmLM0qX6os7PTqTkeujuYJj3RWB0XtYZcC5MTQUAQEAQEgQpD4Mknn1T6Ng8++CB9a9GBg7bNLbfcQu+//36FrUMmEgQqMwKSPaoyXx1ZmyAgCFQJBLLOnVeEjWubO6n+li1Ub+1aMmVm5Fp73Izp6iE+aOJEanBgH4FkyAy7RIlLF6t2IDtA2CDkqe7yP6kBe5H4DHiUnOrWtY4TNGs2hW7erEKjrIWWg7JYQ94x857XXraMav9hJqZAZmAteGmEDdrHfGYmRkJYKDmUfz1zbtSIYufMYZLC7KmRd8y85/Ezv1dkSeDkyRS6bSt58Rc3W6ILhAsIG5AkoRs2UH0mRzBHzBdf5hoKhE3wzB+owe7dqm0Ce8aYMszX5GrXotZXX5Pf66+p8dK2b1fXI+TbKeo8nceDgaBpeOiQmh+kVOq2bZT655+qzn/sBwoXEHcut95qxQlYgbCBXW0NqlE5v4HsAGFzU7AnHfqiE20b14Hi2GvF1sb+dkqRKZOG3Ep4XZzajUC2JKZk0rR/Lto2pRqejmqcOS82V+Xzt5m9Z84z8QPCpnGIJ+355B7aMb4D1fJ2pgmLT6p299zsQ+endCUnvR2t3BVOz/esRxemdCMQSNuPmT270CZiendVvuDVVoqUeffnY6p/k0A31X/cU7eo86nDb1PnGFMjbK62BtVR3gQBQUAQEATKFYHX+McpEDGHbX5U+emnn+jhhx+mP/iHqBdffFHNDyJn/vz5dPKk+f+Jcl2UDC4IVBEEzP7FVWSxskxBQBAQBCobAlknjqslefZ8QIXx6DmtpXv3brmWmc4hPnq/GqRzd6N0TtntGBCk6rNPnVKfGfv2qU+foUM41KauCqHxG/0++XCmJnjZXM3KYg1Xm+Nq9cgsBSIKhApS4NoHB5P7vT1Ut/TjJ67WXdVnHDmicHLr2lXt27Nf/1z90o+bsXZu2pSw54yzp8mxIXsysWeOIdb8gI8OIHJcmERD+Jgbh5jBsi6YSYarXQvV2PLm3qmzuh5OPFbg11+RE6f1hRkzM1V4ViJ7V+k9PFUZwqWKayVZQ3HHLGm7PefM4UQD2gVSDQ6FCuVwpu4tauUaZu/pOHJ1tCcPF716rTkYRXVrmMOeDl9KztV2KIdMYZwet/oTvF0OnEtU9bvOxqvP2xt40d4LCXTwYiLdFOShvGLCOewpr73Zq74KiZoy7Db66PFG1mp4/czedJnWHo6mOjXdKCUjm1IzjNb6og5KuoaixpI6QUAQEAQEgdIh8ACHPG/nH0Puu+8+GjBgAC1bvkwNNJl/qIG2zdKlS+mFF16gXr160W233UYzbTxdSzej9BIEbhwErv40cOPsVXYiCAgCgkCZI2BMMj+86oMCrGPrA82kjFZgTE4hQ0w0hQ17TitSn8aMTPVpSDA/QOtr1MxVX9yTslhDcecqrJ0x0fyQ7hBgg0OAhQSw1BXWVyvPTkwip5Bg7ZQ9U7xVmJRWYEoyp3NWIUt5vswhjEoze5s1mCyhMlrd1a6F1g6fjrc2VafQoXHr3EUdZ4eF0cWHHlJEEULcTIZsc5ts86c6ucpbSdZwlaFKXR2XYl5vPf8c7Zl6/i65xkvLMlJqZjY9M2V/rnKcpGUacpWF5umrVcanmomVBRsuEl62lpLOdV45JfCugYYN7M56rLHDL9iw7w/Tsp2XFRnk5eVE0fHma53FIVHFsZKsoTjjSRtBQBDIj0Ach8+6u7sT0jrD0vlvMl7ellDa/D2kpLoh0KVLF8Lr119/pUWLFtHIESNp7k9zadCTg2jMmDHkxD+0IETKyBp9H374Id17771K3+b222+vblDJfgWBfAgIaZMPEikQBAQBQaD4CNjXqKEaZx49RhDFhRkt5II64TdHFrCFFwpChuCBktcc69RRRWkcJgUNnJJaWayhOHPaWUR0My0eQrZ99LXMBE3anr3W4sz9Zg8ifVBuEsvaIM+BI5M8qZxVAqLKEA5GSBO8aDRzrBuqDr0f6U81xozVikv0ebVrYTuY3tOGUbBUxM2YodZUb81q0gcFEwSFz3S4x7abOta7uSqxYpMxO5+3VEnWkG/gMioI8XVSI+0+l0K9LN+HY1Oyco1eu4YLHeZQqM3/a6/KG9bKTerkalzISeNAV1Wj6dMU0kwV+3iY12TbBqFNIGy6Nw+gH4c3I3bioWe/O0TLOZTK1tydzF9nDoel0APmCC1rdUnWYO0kB4KAIFAiBF599VWqzzprmg4JzkHYjB8/vkTjSOMbH4H+/fsTXhAhRhgUyJuffvyJ+vXrRwih+vLLLxVx065dO5rDIdY4FxMEqjsC+rFs1R0E2b8gIAgIAqVFQO9fgxLnzaP0Awf44VxP6Zz9KYE1XYz8CyM0WfR+vqTz8KBkzhyV+s96DuOJJiN71qTt2E7QPYEXhwOHVMVzXHfqjh2UfeKEEt/NOHiIsqMiyZGFfo1x8Sxuu5YyuS5j107KYNLEgUkSeH3onNk7oWGDa15Dcfavc3Ck5N//oDTeI7IjmTibVfqe3eTEma7wNJ197hylsh5PJserZ3B53MJFBM8b39dfJ51ef9UpDNHRlLZzJ2VymJTOYKCEWbPUXp34QcCN3an1nEkibeNGSl6/gQycocnEXk5Zp05SFmvdACdY/HffkQNCs9i9GpbOJFAqaw0V61o48v5W/MmhT7spg0Ox4LFjiLxCjjffrMbCW9bp00rDxiEkhMVp4ilqwgTKunSJdA72ZOftY11HBmvepPE9YWDxaFNKKt8X+8mhhj+HyLlf9X6wTlaOB4HeLjR99Tnad4bvLU4j9e+pBJq19jwZWAR40D21KcDLkTM56WnFnkjOChVBP28JozNMoEQnZdKaQzF0M2vJuDrqaeupeNp6LJYGtg+hYB8z6TJx+RkK8HWhx9sFUbCvMy38N5yzO8XRVs7wFMW6OQcuJdFZTtF9S5A7IfvUyoMx9M+BKAJl5OTkoDRrMD/MgT1vpq46R3DKqentSMv2xtA81sjhUyKua8ChUp6ccQrZpGb+fYEO8HgRPMc5FlQ+fjmVmrGg8tXWgKHEBAFB4NoQaNy4Mb3zzjs0aNAgCrscRv9997/0ww8/KO8bjLyT/7afP3+eQvhvpw7sK9s5/j/jbxZ438X/b8SzUH+o5e+4qpS3Gx6Bhhze3JezT97JCQTOcIjx119/TVFRUXQr68EhHXgt/p6zkf/PRzYpNze3Gx4P2aAgUBgCE1kTU7JHFYaOlAsCgoAgUEwEkOY5fNQbygMDWY/cmWBIWLzYmj0KwyRzrHbUxEkqTEobNuTHOeTMabFhaTt2UszHH1P66VNatcoeFcCuwhnssXLxiUHWctuDWuxx4sGeJ9e6hqzzF0jHXiEFmZ49hTRtHaTajuFMTLbZmepvgsiut/I6iXzrbUreulUNA1Kq1oTPyYmzQMDjxMBzFGQm1u1BOm1TahpFvfsOJfKXNRiEmVP//Zc8e/akmp99psqMEVco+pPx1jaqHevoBH3/vapH9iiX1q0JuMFAhkV/+mmxroXjrc3oTIsWqp/tG0SHNQOBFvHGa5TK2kQwL/7CmRURoYgcF9baCV6wQJVjnZEf/o+SWTBZMwhRu3bvrk6vdj9ofcrzc8nuSHrpuwPWrEsBnAHqYmQy/TO2HUHgFzZ9/UX6lAWJYQiV0uwXFgTu3NiHvmBC5fMlJ+mPt9tYw5lqD19Ht7N48PLXzPc20nq/8MNh2nkyVuuuhInXj25DL887li9sanC3uvTJIzlE2eQ152nysjNqfjf2qBnavQ59zcQQbNrzt1FfixbP12sv0qRlp5TeDepqsODxoU/NmkZFrQFtxQQBQeDaERjBmfXqsOfolStXyNfX1+p1A6HZmBiz7hjSOCM8Zjv/cNGfdcvatGmjHs7hpfM6E/xi1ReB4/xjyfLly5UocXh4OKWlpZErf6d65ZVXCPeWmCBQXRFA9ighbarr1Zd9CwKCQNkiwB4KBg6V0XOGIJOJ9T6yDEoIN+8k0GUxJiezaLEnp/vO/8sRiAtjQpzywNGyDeUdo9Dza1jD2Y6dchFKtnMgoxUEkm0tm7+A61iQt6B9YA+mLNTlhBeBFDrfq6ftENZjiDTX27Deem5KNaeOTt+7j8Kef56QrcqXXe1zGYdQGSOjyKi3I3s/PxVOlau+GCdXuxZFDYE9Es8NsWPrMbQcOKzL1sxzJPH15BTmTGzltWtZQ96xSnOO7NoRiRkU4OlErUdvVWm7z3/D2ZzYcyWvxaVmU0JaNvm6OZAnp/suqWWzBs3l+Ayy51/Za3k7wVGmRBaVmEX+ng6EcVIzjeTMa9Q0cLSB2GmIwuLS+d8gUSBntHLMs49rXYM2j3wKAoJAfgTgLQGRWRgEZ318fOgU/xDRt09f2rx5syqHV8UGJrO3sAfku+++S5MmTSKEwYj2jYJH3iwIwNPmiy++oEP8o4mePXXhlSUmCFRXBIS0qa5XXvYtCAgCgkAeBLKOHyMjE00FmcPNN5Edhw5dixmzsiirkCxSdg56cmjUWIWNZTO5Y+Bf1xB2FT9/gSKSai9cqLx1rmV+6Zsfgf0Xkwk6NmGxaTR382XaxyFMtzfwodVvtsrfWEoEAUFAECgGAs8wyR7EOmYQkoWtWrWKhg0bpsKitO5fffUVQVx2ypQptHLlSjrCIbEIrRo5cqTWRD4FAYUASD2QN9988w316dNHUBEEqiUCQtpUy8sumxYEBAFBoHIikMQptK/897/WxSHUzJ/Drdz7PWwtk4OyQ6DBS/9YQ4kw6m31vWnWc7dZtWnKbiYZSRAQBKoLAqNGjaIA1gPTQp0Q4gKCBkRN165dFQzIMIVye3t75UWBlM8//vgj7d27t7rAJPssAQLr169XOkgdO3YsQS9pKgjcOAiAtDGnW7hx9iQ7EQQEAUFAEKiiCLi0b0/BM38gO9YtsfP2Uzo3Kl1QFd1PZV/2DyObcxSfiTxd7ekW1rBxL0XIU2Xfo6xPEBAEKhYBOw4R1YSGMbOLiwuNGzeOQMzA4wZ24cIFmjZtmjUrUCCL8Q8fPlzVyZsgkBeBTp065S2Sc0Gg2iEgmjbV7pLLhgUBQUAQEAQEAUFAEBAEBIGKRSCBMyc6c8ZEJ9YCgyFjFPRKPDjDopggIAgIAoJAwQjA0ya/0mDBbaVUEBAEBAFBoJgIZEdGqmxQxWwuzaoZAgZOh5tx6DAZWci5qlk8ixGvOxxLiekF6x9dj/1gLVgT1iYmCAgClRcBLxan1wgbrBLiw0LYVN7rJSsTBASByoOAhEdVnmshKxEEBIEqjkD22TMU/tLLlHH2LDlwTH9dS+rqvNsyZWQUmFkqb7trOQcpkMaptz0feSRX1iLMHcvpsN1atiLnjveUaoo0Tnedtmkj+YwcUWAGrFINWoxOySxYmX3xkrWl17NDrKnIrYXlcJAwZw45sLCmlq77WqdI3bnLqt3j9/xz5POfF6kyh4ElpJkJmgFf71Vixdj/vx+35wxSLtcKRYH9t5xKoDX7o+g/3euqbFEFNrIpjEnOpEFf7VYlEFJe8J/m5M0hX2KCgCAgCAgCgoAgIAjcCAjIt5ob4SrKHgQBQeC6I2AID6eLAx4jQ2oq1Rz9Hrnc1S7XmkDoRI4bTxn796s2jsEh5DnoCfJ+8slc7crqJI2zL0V/NZlcO3cmR5tU0+n7D1DcDz9Q+r//UnAe0ibzwEGKn/sTeT87jBw5Y1RhFjdtCoF4cGrVitwqMNY8dcNGStu1k7IiItTSvJ5i7JxL9t9YJKcOT+FUs3nNITCIQn5fmrdYnSuSi1PSlhVp484pce2Z1EuYPZtips8gQ0oK1Xj7nQLnvt6FSKHd+YNtahmX41JpQPsQeuyuQAr1MxM2b/xynBZsuEiDmWD5sJ/5nnly6kE6dyWFNo29q1TL33w8lqavOUsP3RlQLNIGa1nyRmuavy2cFmy+RB0/+Jf2fNKhxCnFS7VY6SQIVCMEkN0J4U1ffvllNdq1bFUQEAQEgeuPQMm+7V7/9coKBAFBQBColAjEzZmtyJjgH2aRy52tc60x8+QpuvBQX1Xm+/TTpPevSUnL/qD46dPJ84Fe7Anjm6t9eZ44t7qD/N8YRc7Nm+WbJpPDdhJXrCAPpNUsgrTxe30Uue3aRa5t2+YbozwLan4yXg0fPX4cxf88r1RTZSclkY7Tl3s8nDsjld7NvVTjlaaTjvUcXNq0IZfWrenyc88xUfYzeQ99huxr1izNcOXa59cd4QSyBjaka10a/+jNueZLzjBQlslEP6+/RO8/1JAc7HSUlJ5FiWlZudqV54lOR9T2Jm/1cnXS06y/ztOi7eGKXCrPeWVsQaA6IHD58mWaNWsWLVy4kOLi4lQ67+qwb9mjICAICAKVCQEhbSrT1ZC1CAKCQJVFIGnxEnLltKZ5CRtsKHbSRLWvOvN+IcfbzGSJ56OPkiE+1krYGLOyKPWPZZR1+hTp2AvD/f77yd7fX/UzxsUT0mG73H03ZR09wuFXZ8it+73kdMstqt7cyEgpy5dT5tGj5NikCekMxpw6Pso6f4FS//nHWmZKSLYeG1kcMmnJUsrgsWHJq1cTiCaYY5PGimDAccoff1B2bBwOlZnS0/OFeWXyGCnrN5IpPY1cW7TMFYKVwmk7MZfTLU0pZd1acqhZi9zu75ErxCr++++JWOsFxJZbt265Qru0ea/1075WLfJ77bVCh8k6d5aSV64mys4kjwceyNfOxMRP4h+/kzHsMu8PKUh1lHXuDHmyp5VmwC91g3m/jnyd4F2TLwSKs6z4PDOUUrdto5QVK8lr8NNa90rzOZPJGM3e6FVfO8z3mZqZTYt3RtCANoH56lCwdM8V+udYHLk76qlPy1p0Zz1Pazt488zadIn2nE2kO0I9OaNV7nsXDSMTs2jO5st0NiqFGtRypWGdanN4lt46hnaANYK0mfnPJSFtNFDkUxAoBQIga35gr8xFixZRSEiIStFtYoL2VfZWFBMEBAFBQBCoWASEtKlYvGU2QUAQuAERMMbFKi8blzZ35t8df8lN3rCB3Dt0sBI2aKRzdSF712Bze6ORwjlMKu3QIWv/+BkzKGTBArIPDqbs6GiKmvA5uaxaaW0T+933VGvMWPJ4pL/qEzV6NCUwsaOZY5262qH6zL5wnuK+/858zBk7PO+910qoGJgUQp3JIoybvGqV8kZBY0/2SIFXCCxhyWJF5mRzf5jbPR3IjoUlNUv56y8Kf/ll7ZRiZ84keBb5jhqlylKWLqVU9tDRsXu9FuKk/+orqvfP30RMYBDjEMf7RoiZsg/Gku+wZ8n35VesY5b3QeaJkxTGYWtYg97VlRJ//yNnPTw5NIEQBpfJeKLe8OOP5NK0KWWyjpFG2qRzGNelF0bmWqrX33+T/+ef5yrDiVMrs1dW9sXz+eoqQ8HlqDSqXdPsheTnVvBXBie9HTUI9qDp6y4WSNq8/NNRFbak7Wfm2nM0eeit1rZPTNlP6w9EqurFW8PIx81Ra6o+z8WkU8fRWyjDhsyZvuY87RnXPl+acqwR6w2PScs1hpwIAoJA8RCwJWua8A8AQ4cOpalTpyrSpi17Vw4YMKB4A0krQUAQEAQEgTJDQLJHlRmUMpAgIAhUVwSyws0aK3r24Mhrxivmh1HHm3OHldi2S165QpExnr16UX3WW6nx1lsEYgQEhq2BXKn75wqqzyLAIAySVq1Q1VnnzivCxpVJI/Svt3YtEzAZtl3JhUmj0M2b1StXBZ84hNZV5f7/+5+qCpg40drW18YjJWjWbHM7Dq8qyGI+M5MSIazVEsreOs6NGlEsi/gawi9bm2Nf3v0fofp79pDf8OFkiImm9H17zfVM3NTbsYMaHjxIddjzx/WuuwjkVDaHbZWlZV28SBEjRlhfCbPnWIePn/m9ImkCJ0+m0G1byeuxHO8ZNEpZ/qcibHyHDFH12Gva4cPW/jiI/PRTsuesKNhDgx07yatvX0pgEWXNe8m2sZ2Dg2qbdTnctrjSHEcnZVCwr5N6FbWo57vVpiMXEuhEhIVwszQ+G52mCJsa3s60+X/tae5LLVXN6AUn1OfR8BRF2NwU7EmHvuhE28Z1oMzs3J42Y387pQibSUNupYtTu9E7/W6mxJRMmvbPxQKXhPVi3WKCgCBQfASOHz9OY8aMofvYK/AQ/4Awbtw4GsF/Jyfy/wcprLsFA4EjJggIAoKAIFDxCAhpU/GYy4yCgCBQjRAwmcwPoHArL8yyDh9RVV4DByjPFa8nBqrzDBvPGxR4dOlMDnXrqJAql5YtKeOg2TMn68Rx1d6z5wOqvz4wkNy7d1NlFfVm4i/1mWGXFNHizALF8BByv7eHmj79uPkBXVuLx8DHyI51ZVy7dFFFGceOaVWE0KMU9kpJXbuGHHgfsKwzZ631ZXEALxrMo72MaTlEQ8aRI6T3q0FuXbuqzFSe/cyeTNq8GUfNBI0Hl+vs7Al7BTmlmcKBvXCc2fvGEB1J6fv3kmP9+qo6k0Pfqpo5QDCmGPZw6wBydbSnKX9dyNV6z7kEdQ5R4Ya1XKhbU19qHOKpSBdkpdLqB7QLpBruDixw7EzdW+QmP/eejlNje7joac3BKKpbw1mNefhScq655EQQEARKjsDOnTvpNSbne/GPBiBuxo8fT/Pnzyd3d3caNGgQGQwGuosJ9N69eytCp+QzSA9BQBAQBASBa0WgYF/nax1V+gsCgoAgUI0QcAgMULs1RJq9amy3rnnfZJ08aVuc6xjiuDD7WuZxQAYgZbiB9V9sTWchMVQZwoksZkwyP7zqg8z9UaznbEgVacbERDUd1q2ZPsDy8G2pQzk8hLSQqrx8QAp754S//rrq7lSvHhlY9BJmzM5Wn2X1BpIlcO7cAofLTkwipxBL2Bq3sOPMW1izZgYN6+AcfJEJCt47MJOlPpk9nvDKZawBlNegZQTvI4cgM0GVt/56n3u5O9KVuIyrLgMCxI90CKb56y9S41Ava/vYFPO1q1MjJz14IGd7OnYpkeJSsyjOUl/P30zEoGM9/5y2OE/LMhI0c57hMCpbS8s0pyK3LcMx1lvDwylvsZwLAoKADQLx/Hdn2LBhtI01tXr06EGT2bsQxA1sA4f0grCBjR07VmWLWrx4sTqXN0FAEBAEBIGKR0BIm4rHXGYUBASBGwwBO28ftaP07TuIXsyzOSZX3Jo3p+RNmwheHLnEgy1N7YPMBEAap+N2Z7LFEBujNF9cW7fKM1jBp/Y1aqiKzKPHyKVtO3VstBBBBfcouNTexUxOZJw+rUSPC25VcKlGTqXtsYQ6cbPM/ftUY71lfwX3zCmN+XIiQYsHqbcRNpS6Zg1dtgnP0lra2TuoQ6TKtmd9nLI0RyaaUjktO/R1oLMDDRurxg5PpGV4ymasNVFpY0qOx4ddrZpqOSCdQn5forxxilpfhgUv+5A6RTW7bnUgWA6eM2sYwTPGi71dCrORXevQHBYB3s+eMbU4HApWr4b5ntp6gh8QO4aosn2nzGRciI8ThXAoE2z3uRTqdbs6pNiULPOB5b02Ez6HORwK4VXw1inKsMazV5KpWah3Uc2kThCo1giAsHnqqaeof//+NJ2zGPr65mQwtCVsVrG+2WwOAe3UqRM1svEorNbgyeYFAUFAELgOCAhpcx1AlykFAUHgBkOAXUZ8Hn2E4hYuoswDB60P89oufV57lVKeepoucsYo32eeIacGDZiUucKZlOKVSK/HfT0o5ttvKfqjjyjz+DFK/Xu96urB4U7FMadWLZU3SBxn+iA7PZlYGDbxl19ydU1jQskQecValhUWRsnLlpEdkx6u3burcoebGqjPuJk8TnIyOXDGEJ2Li6pHBqvUzZtUfebhQ+oTYUx61nNxuu12Fbbl2bOnShke8cIL7ClUS+EBzxtHzqpVHLOvU1tlv0rnLFdGJk1iv/padUvleUCWaIQX2sFiP/yQXDvcQ5mXLpDviy+ZxYxVTdFv2VeuUAKyVNmYzsNdCQm78sMJNGoiXnyRPPjX59SNG21aEblytqhYTn8bycLP3vzQk3X5Eosr787xxuF7AeLL0PKJeGowuXbrTvA4wrXWhIqtA3LIXMIPM9WpO98DldGe6hhMoyykzeQ1F+j9PvUKXWZdDm1q3sCH9jFpo9k9jX3IzcmeVu4Kp6FcGBaXzt41mdSuiR/Zs3dO5yY1CCFYM1efITudkRz43oW3jq2NvLcOvfDdQer56Q7q1aoWteQMUxEJmTSYPXsQUmVrWCNs0D1mgsi2To4FAUHAjIA3a279wdkA85otYbN9+3bKZi/H5ZyVcNq0aXmbyrkgIAgIAoJABSIgpE0Fgi1TCQKCwI2LgNfQZxRJEfbsM+T/wQes7dLGms7bmVNfh/w4h2LGf6IyKgEFhNw4szixyZhN9vXqUyCLPkYyaQPhXZgfZ5Ny79dXHV/tTefmRoGfT6DwUW+oLFMYGymmE2zc2ZEdKvXff61DgZhIe+cdJYIbaiFt9EHBKiNV7PRpFD1limrr3KChIm2yLpyjCG5va1GTJqlTZLGC1k6Nd94mI/+Ci2xZMHjN1OKsV/CaUZYnHsrOcq6zfPqOGE7RH49T3jXYg9/rr1Hkhx+pfeg5S5VG2ng80JvSd+9RBFHiunVqaM9BT5K9n595nqLeeS6EI2lr15pCxwakivfTgymLdR0wrsr6xSSNY3AOAQANGz8mpUCyRYx5X+0R9bbeNj4vMYGk11MC60Kk7NunTUGejzxq9t7ha57FOj/xs2arECqv/v04nK1yhkcNvCuQPl18Su1hyopTZMg20BN3B9HNAWYPGuZdctkL3evSMBvSxlGvo7mvtKCh3+yjFUzcwJrW9aKpQ5qqY3jufDXsNnrpuwP0zZ9nFIHT6fZatHZfhKrHW79WARSdnEWfsiDxLxsu8stc1bKeF3VmUggGAeSft1ym6WvOqtCoJ9rmhOmZW8u7ICAIFIUAtG20kKgTJ06QCxP2n7KoerNmzagj/x0UEwSqEgKZnA0zgUPMa7AnMr5jxHG4NXSaHLTvI1VpM7JWQYAR0LE4ZuHqmAKRICAICAKCQLERQPjT5REjVUYkPMjXWb0qX1+QNIaYOFIhTRaywtqI/xxnx8aS3sfrqmE11j62B9zfwOnH9T6+ZDKx3keWgXROTrYtinfMXi7GyCgew0g6JkIgGlwSM6WmkSkr06pdU5K+aGvkL1p2Hh5mgoNDoEz8JUsRP3nwMqWmkCE5hfQ1fEuHVxELw9gwnasbYT86Z8aRw6U0w3U0JSQqfE/f2YZcbr2VgpmkyWtIB29KzyAdf3HUyKsU/oU7/N13VVPvJx4nv7feLPP1513HtZxHJZrDlfp8sZvORJj1l7Zzlid41pTEQLw429vlS9ONMfBNJCIxgwI8nSjLyP8Osk3k6pSDtzZPXGo2JaRlk6+bA3k6m0O1znNK8Dbvmr3A6gd40NLXW1JNz9weOFp/+RQEBIH8CBxmEh9Zo0DUgLCB4aG3c+fO9PLLL9Oj7CUqJghUJQT+/PNPGs4ZKn9hr+P27dsr4vHLL7+klpzEQUwQqGoI1K5dm8TTpqpdNVmvICAIVFoE4AlS75+/KfP0Gco+V3DGI4gM2/v7F7wHJiWK5S1ScG9mGHSk9zV7m+h0/Oedw1JKZUxO2GkiwqUYQOfqQjoqWnukqGE1oWK0gRdRHmcOa1cQKvb8Kg/D2JphP5pBOBii0ibOQJV19hwlLlygqtzvv19rkuvTjgm0vObUjAkeDjdwanqrEjrOW1/Zzv0tBMjWD+6iM1FptPMMh3q5lPzeyhvKZLtP8HGBXmaCEd45eBVkPq72hJetYS1IB35nA2+qn0fE2LadHAsCgkB+BM6cOaMIGz8m6PfZeAb++OOPisQRwiY/ZlJSdRDAfQzSJq9t3bpV3d933HFH3io5FwQqJQK5v/lUyiXKogQBQUAQqEIIMOHheFND9apCq5alFhMBU3SU0iaybe7H2jYejz9uW1TkMcLh8KqKBlKkshEjIHEe4zAuMUFAECgZAhEREcoDIYT1y/61CZ/FKDNnzqTnnnuuZANKa0GgEiHQokULAjkTmSezJ9LXX2Ftu1T+8aVdu3Y0Y8aMSrRqWYogUDACQtoUjIuUCgKCgCAgCAgC+RDQ1fCnINYHQsiYPWcN04fWqdShTfk2IAWCgCAgCDACiYmJ1Lp1a5UVah1reNnaggULyNPTk4YMGWJbLMeCQJVCAOF+yJA2b94867pPsqcsPMqOs3ZddHQ03X333Ur7xot188QEgcqMQP6A8cq8WlmbICAICAKCgCBwHRGALo3rXW0J4tL29esLYXMdr4VMLQgIAqVDIIvDPJs2bUrwRMhL2GDEn3/+mR4vgfdg6VYhvQSB8kdg0JODFGljMLDOHxtIG4gTu3Kyg6DgIFWGEEExQaCyIyCeNpX9Csn6BAFBQBAQBAQBQUAQEAQEgTJAAIRNfSac77nnHkXOFDTkyJEjlc5NQXVSJghUJQQacgbMOnXqEFLYwxAOBQ+bbdu2EcID4Y1zKycSEBMEKjsCQtpU9isk6xMEBAFBQBAQBAQBQUAQEASuEQGERLVq1Yp69epF01gMvTBDJikxQeBGQeDpwU9bSRtvb296l7M3Pvvss8rbZszYMZIG/Ea50Df4PiTl9w1+gWV7goAgIAgIAoKAICAICALVGwEIr4KM6dq1K02YMKF6gyG7r/YIwONMZ8cZO/Xiv1Dtb4YqAABSfoumTRW4ULJEQUAQEAQEAUFAEKgeCGQbsik+Pr56bFZ2WSEInDt3jh5++GHq27evEDYVgrhMUtkRcGB9OiFsKvtVkvXZIiCkjS0aciwICAKCgCAgCAgCgsB1QgA6C81vb059+vS5TivImTYhIYE++eQTSk5OzimUo4pDIDWN4rduph+nfEs/zZ5d6nmPHj1KgwcPpm7dutGYMWNKPY50FAQEAUFAELh+CAhpc/2wl5kFAUFAEBAEBAFBQBCwIrBo0SLq3bs3bdiwwVpW3gdt27alY8eO5ZsGIp1I/RwVFZWvTgrKH4F9f/xBfV99ncZNnETvjh5NnTp2LLEHFu6jAQMGUJs2beiDDz4o/0XLDIKAICAICALlgoCQNuUCqwwqCAgCgoAgIAgIAoJAyRBAWJSfn1+uTqmpqbR48WK6cOECrVq1iqDFcPz4cTpw4IBqd/nyZdq6dau1D+rRDqltbW3Pnj2KhPl53s905MgRio2NpRUrVtClS5do/fr16hheGbCdO3fS6dOn6eOPP6bAwEDbYdS4a9asIYRxaYb5zp8/T7/99htBO0Xs2hCY/fXX9My4j6lf+w507IcfaNlHH5IuI4P6sgdWcUPncE0HDRpEnTt3pk8//fTaFiS9BQFBQBAQBK4rAkLaXFf4ZXJBQBAQBAQBQUAQEARIebScPXuW3N3drXCAUGnevDnNmzePnn/+eRo2bBghbAkeOXPmzFHtNm3apMgVnERGRtIdd9xBU6dOpYceeoi+/PJL1eZrJgEee+wxWr58OW3dspUwDzxo/vzzT1W/ceNGdXz48GF1DhJoyZIlak6QO5qNHz9eZR4CmdOubTtKT09XVVjXU089pVJIIztRXFyc1kU+S4jA8OHD6evvvqOZr79GL/Y1h8k1r9+AlnBok4e9fbGIGxA2Tz75JD366KM0efLkEq5AmgsCNyYCv/766425MdlVtUBASJtqcZllk4KAICAICAKCQOVAINtoooU7rtCgKQeoyWsb6MUfj9LUfy7S0j1XKD41x3ujcqy24lYBUiYtLY0eeOAB66T//PMP3XbbbYSHjeLokcADpmXLloo8mTFjhiJ3MBiEaBs0aEAjRoygiRMnKuKlUaNG9O2336q53nrrLXXcv39/df7yyy8TiB5bM5lMatzvmFAAKQAD2aPZ66+/rjyC6tatSyCSxEqAAHtHJew/QL27d6Nje/fSX+wZA6LG1ryZzFs2diw1Dgigvr0fUNfUtl471ggbeNl88cUXWrF8CgLVHgEQ3U8//XS1x0EAqJoICGlTNa+brFoQEAQEAUFAEKgyCCSkGRRRM+S7wxT6wl/00swDFJlK1LxxIJ2NM9L87VE0fDo/tH62i5bsjqwy+yrLhYKYqVmzpvJw0cY9ceKEIltw3rhxY60416fm7YJChET9/fff1L17dwKJAkO41KhRo5THDspuvfXWUpEqCMuBl09oaCjpdDqqU6cOnTp1Ss2BN5BAMC8vL0U+qRN5uzoC8Ql07s/l1Oe5YZSUmERLmZgBQVOYTfvPf5i4CaTBTzyRL1QKnlLwsBkyZAjBK0pMEBAEchAwGo3iBZgDhxxVMQSEtKliF0yWKwgIAoKAICAIVBUELsSm07OzjtIdb2+ij5acoYg0O+rd7RZ6Y3AHGtDjVurWtiE91KUJPdWrOT3bvzW5erjRiBn76Y1fjlNEXEZV2WaZrNPOzo5q1KhBGaxdotmdbe4kZJQCWQIdGs38/f0JhA7K4Y2DhxHY/fffr0iT2Zxt6N9//1WeMEhti5CrcePGKYHjFi1a0MKFC7Wh6JZbblFjQKMGBE9h5uPjo4gjhE2BHIKmzt13311Yc2v5li1brPo71kI5UAiYIsJp3+9L6L4331LnVyNsNNimvfQSNQ4Kor69elmJGxA28JR67rnn6H//+5/WtNifmZmZdOr0qUpNuOF+l4xmxb6k0jAPAvg7CcJZTBCoiggIaVMVr5qsWRAQBAQBQUAQqOQIzFgfRt0+3E5nY7Lp4e7N6OXH21D/zo2o1c0B5OPumG/19QI86dF7m9KTD7agfRfTqe/EPXQ0PCVfuxu5AJ420LHRrFPHTnTzzTcr7xjb8ChkmEpMTFTlIGUOHTqkwp6QJejxxx9X3ha1a9dWnxgLKZ/hGYMQqfDwcKVvo82Bh/wpU6ZQvdB6qh2IG/S96aabVBOMqYUUIIzqD85q1IvJAmQlQujW1eyjjz6iHTt2XK1Z9atPSaHff5xDj308jgKYECsuYaMBlZe4iY6Opl9++YVGc6apktrMmTPVvdHz/p7qfps7d25Jhyiz9iAn27dvX+B4ktGsQFiksJgICGlTTKCkWaVEQMcxyqZKuTJZlCAgCAgCgoAgIAhUSQSgWYMQqL7dmlLrxgGl2sPq7ecoKiKaFrx0B9X0dCjVGFWt0/79+wlCtAh52svaJppB6wYeOM2aNVPl8MiBoUxvryejwUggb7RfkfHVDuFM8LBBOYiY5ORkcnV1JScnJ21Y6yfqk5KSyMPDQ7W3VhRyAK8MR8f8xFtBzRGONX369GJ55RTU/0Yt2/vbrzTwnXdLRdjYYtKbSZqk7GxayiLT3t7etlXFOgZJ0qNHD5owYQI9/PDDhHuwVq1airjDAMgkhvsDqeFxf8EbJyM9g0ASwTp06EDwEoOh3V9//aUIII30Q2YxGPrDKwwkIDKSIdvYrl27KDg4WJVhbHiPrVu3ToV24Z6BQdgaZCbWoaWf79KlCzk7O6t6vMHzC+LaXbp2IXu9vbr34QmGfycI4evWrZu6960d5KBaInDvvfeSm5tbrhDUagmEbLrKIYAfUoS0qXKXTRYsCAgCgoAgIAhUXgTWHY6lL1Zf5ActH7rn9pBrWujyjSfIkJVGv73UnBzsrp9bOx4WQZCEhFzbfooLBh5+QbbYGkiYvKSNbX1lPAa5A+8eEFAa0VQZ11nRa0oIC6OuHMrm7uJSYg+bvGuNZzKuL+vg6JiMW8JeUCUlbuBl9dNPP6lwurxjg8SJiYlRxQjJg+7SpEmTVOayABZEvnjxIj3yyCNKJBuZy0Cm4HojXfzQoUPptddeo1deeUWlgge5g/sBZMqRo0do44aNahykmYcO0tq1a1UGM6wFIXUPPvigmheeYLfffrvKggWPMpBA27dvpyAOD4NBu2fWrFmKCAK5CXHsgwcP0hOs+QNySK/Xq39Lq1evVu3lrfoiAPLO09NTCaZXXxRk51URAZA2Eh5VFa+crLlQBE5dSaPnZh+mQ5eql0t9oYBIRakRmL8tnD7982yp+xen4yfLz9KC7eHFaSptBIEqgUBUIovezj1KTW4KvmbCBht+4J6bKTrJSOOXnbuu+0eKbfxK27FjR3rvvffUw2V5prXOS9hg8/iFeP78+eoB97qCUYLJ7TlF9ebNmysdYdOvXz/lIQJ9FJAGFW19OLSsLAgbrBuixQitMjGp+BATHSD3SmJXrlxRZCD6fPDBB0q0GiFw8KiB58vvv/+uXiBbEFoHw4PvypUrCaFvEL6GFZa5DNnKYBDHhpYSvHpiomMUwfPNN98QspFhXGg3IezunXfeIRcms5DZDC8QNrCSZjQDgQOSCf9m4E2keQapweStWiIg4VHV8rLfMJsW0uaGuZRVeyNbTsZTv8n7aO+5pGvayMwNF+mPfy/T1L8vXNM40lkQmL0xjL5Zdrpcgfh2+Wmau+lyuc5xPQafvv6i+vecxamdxaoXAgt3XiFXN2dqGupbZhvveldDmrmOQ6WYELpeNnLkSEU+IDMPBH4RwoSwDei6/PbbbxWyLBA5EP4tiNCpkAWUYhKEzSAFeGUzpMIeNmyY8sgAUQAPkffff195cUCQuTwN947OYLhmDxvbNWrETSMOa+rbt2+h6cBt+2jH8JhB6BEslDODhbEX0O7du+nUyVNK6Bri1nj5+flZ9ZagswSDODXIEVhhmctUJb9BABv3A1LJI/wKmcy6du1KH374ofLAwbyaaWNq54V9gqCCODHWjfAq24xmIH7gHaR5HhV3zMLmkvKqj4CQNlX/GlbnHQhpU52vfiXa+5moVNpyJIrC4s3/+Zd2aS92r0vP31uPRvWqV9ohpJ8gIAhcIwLbTiaof88mg5A21whlleu+mEmbJg1qlem6QwM8KNDfk6ZuiijTcUs6mK+vLz377LNKswOZmKDRsXXrVhX+0bNnT/rhhx+KzL5U3PlAGiAEBRozYuWDAB7yn3rqKfr5559p3759NGLECCXsDLHnu9rcRS+++KIi4yD2XJb29Ycf0U6+Z5YwQVRUWu/SzInxlDgxExX3sTfLnDlzijVM586dlRfK5MmTqU+fPoqMREd4lYH4ACYIOcKrdevWhY5ZWOYyrQP0kjRLTU1VXjcQPIYIMrzIslmXB6aRfPC8gRVFopU2o5kaWN6qHQIi41rtLvkNtWH7G2o3spkKRwDpXOduNbvLdm7kw+6+9jRv62VqGuJOj7YJJEe9Tq0pkn8hnbP5Mp2NSqEGtVxpWKfa5Omsp+jkLJqx/hLtOZ2g2i349wodsIQ23VHHk+6/zY82sRfOpuNxqn7w3UF0MCyRVh2IoQdb1KLOjX1U+ZojsbTrjHkMJyc9xfK4oX45InVoVNgaUIcwmABvZ3r67kCcKtt4Io42n4inp9oFUoiveaw/90fShiPmtfRo7k9dm5Tdr8navPKZHwE8+8/axPfJ2US6I9ST7mzgTX/ui+L7KIT83R3+z951wEVxfd2roNgAOyio2HvvvcfeNZZYYyzRfFETjYnRaPKPiSYmscWusffeYkkUY++9d0FAUWygIgjfOw/fOiwL7MIu7C73/n7LzM68eeXM22XfmXvPJYQZIeFtgezpqL64J4sPRi3u+tTJTdnFedj3G27Q67AIyp0lPXWp6k459IRNn7wMp3ki281dMUdblDe86DzrE0wbTwTI+VWhQGY5N8TDPaPt0v0QWiY8wcJEP3rWNqyNsfHUA9p75QllSutAbSq6UZX8LtHq9xNpkFeIkKrbD15SAfeM1LRUdirhkVGWWSI+e8Ghb+nT+nnk+6t+L2mdqA/jLZAjPc37z1d87tLQtYBgSuOQmpqWyUELBK7txWep7rvPEi6Ma57/sfOuaC8DvX2bmnZeDKR6RbNSq/I5yFHojZy6+4J2nH9E5+9GLXQmbL1FjmkcZF/als+p6yewXns8gK77hdCz1+GUTnxmm4vPepNSOWRZ/mObCFwX4akXbwdRs7olzT6AkoVy0s4zD+m7FlFz2+wNmFghPG7wgncBUmBDPBWLW2hrQAeke/fu8im/idXK4hAVhicIQni0C92E1GXuaxBmAo0RhDzZiyHUB7oseMGgmQIybtasWTJUqFq1atSiZQtq1rQZgbhLkL0MoSUiTOiXObNpycivzU7YaPsE4qbe8OEyjA/HVeYvbRntfrFixWjCxAn0808/SzFinIMWDQgbkJMgc+CVBLt3L3YvZm3mMoQ7wSsMXjTwYoKBBEI4FfoDQWxozrRs2VJ6whQtWpSGiz575feiqlWqUp8+fXT3A+FSaL9AgQKyHvxBW6gXxBQymqGf0OZRGc0gcMzGCOgjAE8bNkbAVhFgIWJbvXNW0m8QGz0mn6JQ8SOzWaVc9M/JAAp7l5CsacVctLB/Kbrz+DXVHXNQllHddsmYlk79VIt8nrympv87QhEijALXpREr4NTvxCbb1fSgyR8Vo/EiRGWWIFVwvnPdPLRKhEApm9CjBPWu5UHDV1ylNWLxqeoZ2b4IDWvy3iU7rj5kEuRR7R+O0nVBBt2c2oAyigUkrJ5IVXvF9zndmNKAUAZtLPWO/oPlmw5FaMgH79tR/eKteRHo8udZ8j73UFepZ7b05Pv4Fe0YXZ3KCoLQa/C/cn65CeItu6sTXbwbReBlEfPs8u91CaSP16f/6OYmKuoq5tIf3YrJOl+GRlDFb/bTk5A3cg5iruHa4AzMcLUAAEAASURBVJdh5DOrkSyz4eRD+nTOWbmv/tQono3WD62g3sa5Rehf6wlHdfM8rSBlQkLDqXLhrLRleEV57ZAll2nVAd9o9Uz5uBR1FgQobJ8gc7pPPhltHDi+9POK1KhkVqox9ojQ/wila2LMsBWHA2jYwvM0a0AZaiuImbIj99ODp6/JSRA2+Mxmd3aS5TOkdaRb0+rLa+Kb53kG/kNugsQE/soUDkjxPH71Fd1nHe0o+7V3Kfqwihs9eBZK1Ucfopdvop6qqjL9mnrR6NYFVXHe2iACP26+ScsPBNCXPaqbvfcvBLk3Yd4+OjahDuXNEjP7UVwNXrx4Mc5sIRAqxQIVL2SkwYISL7xHOAdEgd+KcBaIqGILjwBs1XG1vXPnjhRZRbYaWKFCheSCEotVYwwZo6ATUrx4can/AeIA4oMw7KM/5cuX11WFp8YQZIUAbI0aNaRuzNWrV6VgMlJx+/n5yTAZnNu+fTuVLFlSelRAWBbeDFgowxBe4u3tLTVeFCkB4gL9wOIX6ZcRzhJXZh9ggz7e97svs/fAewMZfyxp6HN8hvsJUgZZtNQrtqxX8P6AdxPwQKYteNmApALG0L2BGDVCbQYMGCDD4+JrO9r5u3eoYqvW1KBcOfr1HQES7byZ30CcuLQg1+DBcuXKFaNrx5gxz/RD8IAJsDSUeUy/cv3MZfrnte8x51Gn8rLRtov7AfFviBSr7FTaa/X3MQdju7f6Zfl9ykQA32UQp4ZGGRsjYEsI4LcAe9rY0h2zwr7WKZKF7s5oSPkG/Ut/n/Cnz1oUoK/Eq4YgaY5eico4MG6dSA8pFoiT+5SiDlXcacY/9+jndddo1l4fGt7MS14PD4ERiy7SzIFlqGW56D/0vm1VUCw43anhD4do44H7tGBQBape2JWKD9tL+y4/kaTNpK5FCa/Dwtum3cRjMZCKrw/dauem71c+p82nAqlrdXfpAQTCpmaJHJKwuSuIJxA2xTxdaPln5Shc/FhuNfE4TVp/nUmbGGib98Bl/xBJ2BT2cKENX1SQniQNvz+sawSeLpiD7QV5eOjyYyqc25ku/1Gfvlt7ndYc9CV4puQWizyQL+K2CU+uYBoixKpXCPJvcMN8VMgtvfQ2AWHTuZYn/SqInP2CjPxIkCMgEZWNWnZZkh2bv6lKBYXXSr8FF2nv2Qd0RnjflMuTSRWLdTt+001JtkztW4baVcpJP4n3M3fc1pW//eiVJGyyC+Jp4xeV6I54333qSRqz6pqOtPn8rwuyDpCV3arnpuNivg9ZdIkypo8iGnWVxbEDwnT3t1Wp6qj99H8t89NekekHhNhT4f3y7FW4UfMchM2i/ytHtQtno+a/HJe4hwrvof71POSrz9wL8vvg+pT6lDbNe+IG3dp18bEkbFqL/k8X41DeeHF0mU/ZCAKh4anIM5erRXrrnM6RnDOkoysPXptM2qBDICF8fX3l4g/iuHiBrMFLvVfH1Hucw2IRC0e8sCjUf4G8ic2QwQYeN9BMgddGXKbNvIPFqtZatWpFEIvFcRAwc+bMkeQC9EDQRxAJKoQFixEIJMNTZ//+/bR48WLasmWLJBqQehvZd5ABCwtzCMgiPTNCg8oJMmHEiBEyVAVZfpRnA3RMkP0HIrTwKkKIDGzbtm1yi3TNIGegB4PsPNAtwcIZW0uSNhCXRWgTNE3uCLIsqQxittB/gS6NKXb2cRA9FPfl265dTbkswWURKpVT6M2gTVMMBJchA3FirEFbBmFLxphK260la9R1ijxV7+PbMmETH0J8Ht/XmJ9sjIAtIsCkjS3eNSvuMwgbLMJm9CtDPo+jfnievvmE8CTfWSwsd50PpHwihAV2USyeTbX8uTJR87LZ5GXzB5Ullwxpjaoivj50qwbS5gqtOeonSZsNJx/IervXikopeeJ2VDaGsgVd6fS9KC8OkAMHhNeCv/AcyCW8O9gsg8CpO1F4dxZhagh1wqux8BrZePi9aKG25cHCwypLBkf6Uuga1S6WlZyERwsMITm7LzwWYtfPyEN46oCUu+wXLEmbMyLsCjZIkDiYvwh7g9dOkLi3sGev3kovnNJemck36KV8FfdwlqTNeZ/nRpE2V0Q5fA7gbQIbJPSXtKSNGmc7QWyCSMILJCH6ifYRaQgvmTw5M0miEnXULJyZTvxYA7tGW3pBomR4503mKhbCKnxM8Flk7DwHNiqUqXG57FFYBrw0CofqBaN+zO88FkAfCw8neAi1r+QuwyWNHgQXtEoERGSeCJE17js5IQNwdk4nvmvTm3wpPEwsGc4DbxSkKD516pQkN7CwBzGC8A1o3SBMJD4DGQLvF2TqOXv2rAwbwTUgSqC5Ag8a1Iu64PUAggSeIyBdYCBV4gulQggJ0ihPmjRJkji4DiLKIGWg74IsPmgfpA0MQrEoj6fTIICwj3EhjAhZfbS2b98+qlevntT8KVGihMUXRl26dJHeMMhyBIIA5BXwAOkAHPACMYWFv/KegtcJjqE8yCiVghy4wvtIq18D4gueQ7i3d+/epSpVqsjwHngfqeu0449vP1KQfxlE20lli0W4HgibASLEjo0RYASiEIAXGJM2PBtsFQEmbWz1zllhvxGyop6aQ4dDaXG8Ek/gEQrRd8bZaL1+9Sb2J5TRCmrelCvw/mlPi7LRPXI0xWLsxtcHV0EoIcTj+JUgQqjMuqMB0suihdCtgT19GRUHi9AsbXgWzoW8FufedwuH2MyIwJOQqDCa/DmiyD5UnV94usRmlYXmDQyaRkrXaKnw5BouPLlgCAl6IcKeYG/ePSWHlwksvyBKlOUS8/k9aRNV/vydp2IeR0+n+tLIefxSrGizCbJDGbR4tJ48Qe/GmTd79D6AtHki+vsuapDcshi/KA4JixqXajO+rbHzHNgocxSLEVNMejYJb7mpO2/TvtMP6J9TATRGpIhe/mUl4bmT2ZSquKyVIQAuMPjVG4v1yu/BU0n+W6wBEyuG9w48W5DqGAZSAMRF06ZNJcFhTEiHahKhR0qzAxojykDagCQA+ZDbI+ohwq1bt6THTMGCBXULEJUWWV2HLUJPtIb+gLTQLlqU98ySJUtkUW09KnwKXhbazDvafVU/9ERmzpxJ7dq1kx4269evt6inDdqFKDReiTVkHMILhnu5detW2iNIj2xC0Bdpqrt07UKFChZKVDPwZKovSLyvhXfQLBFeZUlDaNRPK1ZQwdy5abTwnmJjBBiBKASgaWPK9zLjxghYEwJM2ljT3bDxvmQRi2FDlkcsQi+K0JMDP9SS3gOGymRyipqKF4VQa8tyhkpEHcvuHCUqG3sJw2eM6UOP2p4yzGOFIGzOCO8gaPQoEqpYrgyyYqXTY7gVPmoJBDyzRs2rk3eEQHDZqBaCQmJP/Qv9IX37QYRKQaPm7K915D2FGPCXIkRKmce7NhBuBO8V2PN3JAr282SJIltA+JyaWFs3L3DOWMsq5q7Pw2AKF/pNEO0FOaj0n1BH/uxRc+yQEL/uV9dTVnvmxhO59RThXQ7vXHrPitAthCI56YUdoWB6p9T06kGYro2j16K8lGQlRvwx1zxXnjxnhDedIm+1zcNbDi9gset8EH084xT9se0W1TZSH0hbF+9bDwJpHCLo1WvTyXhTRnDL/zl5ZY7y1jLlOnOWXbZ8GS1auEjqnMDDA0K1TUS2HoQxQfslIQZPjjFjxsjQJm24D8Kh4AmCTDoBAQGSdEGYE4iT3r1709FjR6VwKwgaeJQgxTE0WOCNs3fvXqHzFrfwJjyBUH7q1KmSZIE+T1ymzeyDcSOzj6ODo9TvQdgWPFQgEgsPoK7vQoHghQTPF+jsWKNBXBceRyqEDh5Sv4rwMmRDMqdNmDBBZmcaJAiuGSKFvKWsx8SJlEt4Em1YtYpIhK+lRMO8nD5tOvXt21fngYZQOoQ3WjJsLyVibUtjZk8bW7pb3Fd9BJi00UeE35uEwEkRVgJNDwgA+4vQjfn/+VFF4WWj1fgY9EFeGjz3PDUXWjMtKrlRReEJEfDsDfWu7aELzSibNyoV5Owdd+ipWJAXdM9AGdM4ylClrSJjyKm7IbJfp8SiGm20FAs+N01I0kKhdfM2IhXdfBAVcnX0xlOanz4NFcqZXmbFMaYPLUUGnKFCOPWHlZdlWz2EwLGyaoUyEzyJdpz0F9opb6iByNiTLm1qGYbTQYR2sFkOgfrFs0uPlPk7bwlvkwiZ9Wilt4+uQWjW/H3+Md17FPVUGfMjd2YnmXlMFcK9u3n/Ba055k9iqtJPgsSBbTweSLmzpqfWYl5CAHjAvPM0UIQt3Qt8RbcCXug8YcCXdKjpSeuERk7d74+IeZxThPkJTxyRpcxYIeoPyuekBf/cpRaTTlLHqu60/dRD1T25rSOyN2UU5CW0oT4WR+4LkW7o7MADDCQPDDowmwXhVFoICnesnosKiExsAU9D6bNG+SizCAn7oGwOKcLcY9Z5ypk5Le0SXiywTScDqXL++L1YzDXPy3s5C6xECNjcc0InKI8ImUlNpTydpSfNwRtiMXnpkcjW5kSvQiPpv8tBso8OqWOSbfKEnf7B4hbhHErLAYtuvKBPYqtWq0hWWnsk+rw211iei/8L8BCpXTR58AkKCqJp06bJbDUgIOBRgwUhRIbd3RP/PwBhT/DGALEBUkYZ5sOoUaOkRwm8bcaOGyvnDAgT6OUMHDBQkjoQ1zx27JgkjpYvXy7rgMcPNGyQPjw2QwYfhP8gBApEEHRtPhfZh2Iz9Ec/s0//Af0JIVHKoGcDvR1lyBiETFjWRtpAbBhaPBs3bqSKFSvShx9+KD2kQHxZwoAdws+QVnugmEuzRFpxc9tAQb69EITFRiFobcvfJQqXhGYr02ZgA3EJ3SaE+MEwNxcuXCj3+U/KQgBzQetpmLJGz6O1dQQ4e5St38Fk7v+Q5VdihAv1FgvICZ2KROvZbLHInigEiVXGGJxcMaySLmU33k/b7UOTt9yQGXXwHoKsF4RXQyWRacZXpGHWmsqWo465D9itdqNtVVYbHDSmD/2F9wUWxVg83xAiqlq9MqQ3HyzEZ49fj1pkok5ojniPqYpdNgsigMxNnwsCQGUYc8+RUXqt7B1XQxB1IfTJzOihd4VzOdP+cdV0PUIK7aFCxBeaMAhJ+qJdYZq4/po8D/HhKT2K09drrtFCQarA4JWTV6TTvnTrqS57FLxbRq6+RutEljKth4zvzEaSCLrmH108VFYk/qR1TEVFBAkZLDwQuk4/o5s/5YS2y62AECqaO5MuexSEtD8WZUDWwErmc6Xlg8vqCErU8YXQXsIc1drmr6tKjxYQWH0EQXpWeIohK9MgoTH1x+YbsigySI0Vosawf8dUo1JfehMyU+0X2Kw9dJ+uCPFmED/xzXNkjyop0p3v+KqSrGuiyOyGNnYik9c7QWZ40AwWAsnbRWpyhVUboRs1u09JmQ1umkgFrjWITK8SwsYQjE4pBi8JhMNg0QjDogKLrJ9//tlmITguSPxWIkPaJx2rUn73+MW5TRnovyfukr//I9o7qrIpl5mlLLJPwesCJA08akCYWMpAnCCEST8TDjxgUgnyFl4tWsMiBB4EIJIUAYjzEE52cHQgLF5xPL6FCuoHiWGsgKx+Zh+0FxISQi6uLjH6CBJq9uzZRmn7aMdmyf21a9dKz6bSpUvTt99+S9qwMEu2i7pxv0DclMjlTj/36m229N8gbLYJjywIT4MATKgpPR8QlSAK27RtQzdv3JRzSj8r2Y2bItHE61BJHGKewTMMBg+wHNlzSNIQ91+F/uEc6geBAo8mENcweDnBqlevLj3E4K2FTF7/iDA1fCdi/sAqVaqk85QB2YjPJkgYldUKxLd+BjboF4G4HDx4sPRWa9SokdSgUiFxsmL+kyIQAKEMXSqImLMxAraEALJHMWljS3fMDvoKMVhkqMmaMY1B4VGkZoaHAbL85HJJGyPzjDkgiK8P8bWBBamf8G5wFIt/N+EtAIFYNssjgDkR8DyU3F2cqPKYKCLv7nTxY81AmFBsvXkkPGOyiLmHewZx3/SCUEnjmFpHzr0RE/CpCIvK6ZJGhiBhkaRC5LR1Im316/BIyiXuP85fEyK8dcYK1xIDpk2njdPP34WPuIgwLpAw8NhSnjTqcvQzneiXoVAvlAEWmIOYizkypRXCwtF1ZaDR45zeUZSLFERpBKUTGBkah2rP0NYc8xxEl/9zkYZV4Kj9rGB8SOEMYglCyPoZpgz1x96OIQ0vhF0RmvI46DHVr1dfip6q8BpkqMFCGosYteBG2AzEbrE4xoLD2FTSSYndx/MuUjClpyZVvcza7G9LDlPLijnpl86FzVovV2ZZBEA+QXvn9OnTCRLwtWTvoOdTuHDyzCdF3KQW+GwQxC2yPSXGFGEDr6ZevXoluCqIMYN4QQiR0jSCVhHqBdmCrGSrRNgVspIhgxjIkEWLFkmBamRnA6GJ8vBa8vHxkeLa+M6CtxiIamgGgfwEkXPz5k1ZF9LYQwwbIWoQwcacgbA2BLPhNYbwOnxXwvqLNOYg2H755RfZhyJFishU9BCNBmEDIgjzDaQivmNxHIsdZciKhnZBEnG2KYVKytmCzMTcW7p0acoZNI/ULhDglN92cRttaxDI6oNXbIbFdN6s78VaYyuXmOPx9SG+urHAtnQf4+tDSjt/VoTgQcfmftArWnrAT3pelRWeKqYQNsBMZUrCPsSn9Q3EBggbWFx1a0PzUNZLCCOvG1EFuzEM2Zq0BrJGWWykjLafqqx2Cw8wjzi8UuAxI00U1LanrSO+fXPMc2CoxKC17WF88Y1RW94e9yE2iyfESAWNdM5YTCjCpn379jKbDcaNUA14BeDJdccOHaVeCMohRbQ1kjafNsxDHX89TuWKuAvRbPN8l5+7EShEwV9SgxLJExplj/MvqcaE9OnI3JWQjEuW7mNyETYYF7zqpkyZQp07d6a2//sfbRR6RgklbkDYeIuMYwjfa9u2baJgg7cV9JCGDBkiBbW1hEdsFUOAG9490AZCmN8ff/whi+L7CTo+SM++evVqSdps2LBBehWCxEEYHrJ/gbTBNSBtIPwM0gntw9sGGkr62crwMAKkEcgiEEsQo4ZHDrzODGVgU/3GQh1ZzvBiwkahkrK2HB6Vsu63vY029tWzvY2Ux8MIMAI2i0B7sQgMCX2fCamMCM9Z0K+01YwHZE/NQpxCzGpuiI10BPoh0B2BweMGhnADZBJSKaohUOvv708+93xk2MzHH38sn4Rbq15FZaFp1rhiLtq09xL1bVdReLUJt7BE2HOROW3/ids0rHUhalraMlojiegeXxoPAsjUosSLtUXhtYE5rLzItOdSyj6e+iN1O0iK9j/8QOsT4HEDwmavIGxWCVIkMSFRCvPcIuMUyCQQLT+IPsFzBeSI1uDRojWEm8BUdjOELcGUp46Xl5cMk8IxpK6vU6cOdgnHkVJdawhfwZwBAQWD+LZ+tjKQSnghpBRC4LDAwEBJdKswLG0GNllA/AEhBbFv6D+xpUwEmLRJmffdXkYd/RGwvYyKx8EIMAJ2hcCCQeUIOkbQbrkxpQHtGlk5Tk8Tuxo8D8ZuEcACA+EAeNqutERuXL8hFyTQUMELegwIO0B4wCCRcQYLKuhwIMWytdrQJnkpQoQ4rNp1IdFdXLnjvPBAcKCRLfInui6uwDwIYMEMDwosghNiyOyDeQ9PCiziU7KBuIA48du0aand998T0nUbayBsrvj5CS+8Y2YhbNAuQjKRUQyeMQiD2rNnjxSqhscfyGTce/2sZAg3gqbRPu99klhWHkwIT0J9qENp3eC+IzTJT/QbGcYUgaPGDG0mrSnCD9nTYJg7IPswd5BeHnXsP7BfeiqC4AYJBEIQAsb6Bj0eaxPD1u8jv7csApw9yrL4cu2WRYBJG8viy7UzAoyAGRCoKzIrNSqZVYrtxhZSZIZmuApGIMkRyJo1q1yEqIYRUgAh2rFjx9J///0nX5UrV6a3b99KIU2IbH7xxRc0d+5cdYnVbUt5ZqK/Pi1Dz5+8oF3H7khdpYR0cvnOi3TX7wmtH1I+IZfzNYlAAIteZJQyZPB+wIIe3g0JMYgpI6sVyEeEy6R0AwmBkJ2iwvPGWOJGEjb379NGoStjTq+7kydPSp0fhEVBgwbEiIenh8xKBr0b6IFAcBj3T4VB4bsJ9xIhTRB1hqcM7MyZM1KAGN5EAwcOlMd69OwhCR6QLsi6hvqhLaREgfH9p/XswdhUtjL0ac7sObKeb775RvYB3jz5vfJLzSRtBjaEX+nb9OnTaf78+fqH+X0KQoBJmxR0s+1wqCxEbIc31dqHBJHUEyLLSJWCrgnW2zD3GCEOe+zmM6okXPt1eiDmboTrs2sEfEV2sUtCkPiDElmtZpw8r63mVsTakZEjR0otGxAxyqBhg8UFFkYwaEVA8PP333+X7+HeDzHPAQMGyPfW+ueCbzB9Mvs8ia98qlomD1Urlduorp6/FUTeR67Tm/Aw2jy8MhVyS2/UdbZeSGW+QZgLFrLwSgCBB9MXptZm7cF5lFWLZXg3wAMBgqzK68FQdh7MI4SywDvBw8NDei8gXAkeFbFl7UE/FFmD0Jl06d7rFqHPt2/fpgYNG8gMUhDbhaAs9Gxu3LhByNqDbETKMPeheTN+/Hh1KMVvQW74Cfw3C6+b2AyEzWXhqbJJQ9isWLGC5s2bR0jhjhDKxBiyNoWHh+u8/7R16Wclg+cfMjgpzz+VwQxCxPAU7NKli24O69ejMj5pj8e2r5+tTJXDcZBGWg8dUzOwqbp4a/8IIGwO4v5//fWX/Q+WR2hXCLAQsV3dTusfDLL1dJ52ms6IdMSww+NrCdLGMj/GD954RrvOBtJnjfNRjnfCsnEh9Dj4DXWfelIWgcDtqs/KMXkTF2B8TofABd8Q6jL5JD16ESpTxd+cWl93ztw7PK/NjWjy1zdx4sQYnejYsSPhhVAELIqxkB42bJh84gwNB+0CJcbFVnQAHjebhleghQcCaO0RXzp/M5CKemWjXFmdKY+bs8ychu6+FhnOfB68IP+gF3TH9wldvf2QOtT0pD97Frei0Vi2KxCWVplvkD0HhJ3KfGNImHrrlq3SIwHeCsjSA7FWeGdp60HYChbwIARB2miz83z11Vd06fIlmUkHYrCXL18mV1dX2r17tySM4OUFQ4YgGEgdZBRCn9A31AcdJmigwJCWGQshEEGjR4+WHmIgcSASi2OYt3/++afUcJEXiD8uLi7SQwLzHG2zkRQmb9WqFbUS+jZLxD3SihMjdGqQwNDv2dNohA1w69q1qyTTMAe8RMgV5lJCLa7vFx3R8l5PXzajyBr9NhXpqH9cV4/+iVjeg+zTEn6qmKFjqk19seHY+qjq4q39I8CaNvZ/j+16hMJVjI0RsDgCIn1wZPmRByLd+u+K/HzxpchD155ERohjsC+XX4n0HLA7cvTaa1EHxN/uM85F1hp7WPfe1J0JW2/Jtk7fe2HUpegL+oS+oY9lvvovEn1mYwTiQuD2o1dy7mLO/L7jTuRV/xBdcZ7XOih4hxGIDHkdHjl05c3IGv87Hll42H/ye7b48AOReOHzg1elUQcjP114MXLH+YcpDrE1a9ZECi0ROW4RVhLp6ekZKTysIq/fuB5ZsmTJSKHTIV/CcyZS6IFEitCUSKEHIsuLECPd/pIlSyJ79uwZKbwlIkWq5EiRglmWEYsVWeeCBQsihWdC5GeffRYpwpzkORH2Eok20c7hw1H/d/EebRky4ckg67p//76ublzr7e0t/q9HRIpwvkgREhN57NgxWU4QSZEiQ5rcF146uioFURQpvIoihXeJ7hjvRMr7XKd27ci6VapEbvxlQuST7dsjF4/5LrKouB/1xHHMhdhMpMiWOIvwtdiKmPW4IAwjhXZNjDoFsRcpBNRjHOcDjEByIoDvNBFul5xd4LYZgQQhgN8EnD3Krik56xnc2mP+5PfkJfVpmI9+/rBItI4Fh76lMJHCcZm3L33XrhClESm1X7wOo+evwqKVs+Qb8SCbqhfOLF8ZnBzor3/v0pqj/tSlGmcZsCTutl73T1tuyrkLkWRo7miN57UWDd5P6Qjge/WPzgUEDAVIRKPS7cAwuia8a+4EvqTM6UT2tcJZU0wYlKG5gJAkQ5lvtMLUuE4JU2O/SJGo/6UQsVYZduDdAuFXpE5WhnAphCHB9LPzfPnll7R//36pKQIPH0HEqMt0deoOxLKDMCh4y8DDA15h0CdBOBS0mOD1ABFb1A1T/cS+IBioUqVKNHPmTLxle4cAdFw2Ce+nNkJ8/LOp03W4YH5sEKLFcWnYwOMG86Ffv34UFBSk05LRVWLmHbGQMFijIPEMHueDjEByIoBQupScsS45see2E48AkzaJx5BrMAKB+YKQgQ1vgR/thu3lm3BafzyAOlc1TJRsPPWA9l55QpnSOlCbim5SlFbV9FZklf1rvy+dElo55b1cKOxthDql2z58HkaLDviJxUIIFXTLQP3q5TGoqYM+grSZv9eXSRsderxjCIG/j/hTnpyZYhA22rI8r7VopKx9aNMgzIktOgLpRGhFcfc04gWiMzrZGb1kynmHzDdIR4zMN3fu3NENXCtM3bBhQ3kcYR7Ci0ZXRrsDHRGEQS1cuFCmXAZhg/Li0Z4spg19gR4IsgRB/8bNzY3q1asntUxQUJu1R3jryKw9EBA2ZCAJoBUBUeEWLVpIHRuMRRE1hq7BMZxHyBVbTARAzOwTIWobxXfIaiH63KRlS5kaPGbJmEeaNm0qhaKRle7x48dSHDhmKT7CCKQ8BPA9qLS/Ut7oecS2joDh/8C2Piruv9Uh4Bf4Si5us2U0POWcHFJTQQ9nmv2Pj0HSZsiSy7TqQBTxg8HN332HpnxcSlf2oxlnyfvcQznu9YfuU5aMaaNhcOfxa6o75iCFasic2bvu0qmfapF+NiL0EQtx/8evotXBbxgBLQIPnoVKL5u6cQgP87zWIpZy9iES26d3H0KWHSZtUs59T8xItZlvFGEC3Q94qvz0008y1Tu8J2AQpo7NkJWnW7du1KNHDxLhKYR6//e//+k0TkAC/fjjj5IAgB4IhGtbCkIAJAEy8QwfPpy88ntR1SpVdVl70Bay9aB95Q2EY2gL2inI9gNRYfQTgrQgC5BaGQLHsRm0JTAOeOOwxY5AW0H64mWqIcU2dIdA4CC9NvSE2BiBlI4Aa9qk9Blg2+M3vIK27TFx760QAYi0VvPIFGfPBjTKQ0MWXKBrIgOP1oRuiCRssmdORxu/qER3xHuIBo9ZdU2SNpf9QyRhU9jDhTZ8UYEQltLw+8PaKmjcuhuSsJncpxR1qOJOM/65Rz+vu0az9vrQ8GZe0crijUdWJzpyJTjGcT7ACCgEbgkiEOaR9X32FHVOu+V5rUXDvvc3btwon3AfOHBAPs1LjBiofSPFo9NHAOQMwoSQIQziv9OmTdN5oRgSph46dKiuChAxQj9GvofrP9Idg2RB2FKmTJmkpw3Eig3ZhAkTaNy4cQSCCBmDYEqw9YcffqCvv/5apmiGUDCeUMdWDzJD4QXvGSUAC0IGYV8wHFPXhr8NJ6RpFvoSMuWzLMB/zI4AQpSERpHMliM0hQief2yMQEpGAJ42HB6VkmeAbY+dSRvbvn820/s04odkfNa+sjt9s/QKzfg3+lPEU3eeyUvbCbIFqV/xKubpQld8nxMyUqnznWvkouyZ0shX4wputPHw+9j80yJjVYa0juSc3oF2nQ+kfNmjFtoXRUpaNkbAkgjwvLYkuslfNzQ6hPirDEm5dOkSQeMBIR9YvHJa0eS/P7bSA4REgSBB9ie8kIFJ30zJsISFCcKWjDGVtluRNdprYsvaoy2j3VeEjfaY/j7CrJDZypiy+tfye9MQwPcRvpdKlCgh08JDv4iNEUipCDBpk1LvvH2Mm0kb+7iPVj8K10xp6cGT0Dj7CQHiTrU9aKW3DxXzep/+Mygk6ulf3uzv04PnypZekjZPXobRk3fn8+d47/GQP8f7smj0VVgEQVukrwij0tqrN0IR04Chr9mdnQyc4UOMQBQCBbJFzTf/oCiPm9hw4XkdGzK2fRwhB0uXLpU6HiL7DiFNL8Jarl69KrfVq1e37QFy75MUAQgMI6QJRAs8VAwRKEnaIQs3xoSNhQHWVA8dI3g5IbQN4tXK+0lThHcZAbtHAKFRTNrY/W226wGmtuvR8eCsBgGQLLcfBEvPmLg6NahhXhnGdFZ4xijLnz2D3D107ak6RGduRJ33zOJEniKUCXbyTojufFBI9MxTed4RPgd+qEUBsxvrXisHl9Vdo3bgvYO+os9sjEBsCOR0iZp3+y8HxVZEd5zntQ4Km9+5ePGiFPb84IMPaPv27VK/4+y5szIUBZl7RCplqSUyZMgQmx8rDyDpEMDCGiK+0CKxd8Im6VDllrQI3Lp1i9KKLGJ58uTRhappz/M+I2DPCCBzFIzDo+z5Ltv32Ji0se/7azWj61nXQ/Zlyq7ooU/6HcwnvBfKFYzu0l2nWBbK6ORIf5/wp4/nXqAmv5wQ3jVvqEbxbOQovHPqF89OCL+av/MW/bj5Jk3cdlt662jrHvRBXvm2+cRjNGz5FVp6yI8m/X2HHgVHJ3dQSPWxex1PbRW8zwhEQwARf80q5ZIE34Hr7wnFaIXeveF5bQgV2zp2+fJlGjZsGDVv3pxAziB8xdvbm6At0r9ff3ls7ry5tEmk5O3Vq5dtDS4ZewvdlW+//TYZe8BNMwIpB4ELIlQKKdhBDh45ciTWgUOsmjVwYoWHT9ggAvCyYWMEbBkBJm1s+e7ZUN+7VhN6MyLcaMb2GzRWiAJrxYYF7xLNBjfOF+19WodUtHRoBZkRarsgbuCFUzKfK83sU1KWcxU6NVP7laGIiEiavu0WTd9yk+qUjp5GtEMld/q+azEKF2FSK/YJ8eFFF2nSxut0XqNpgz6hb+gj+vpRdfdo/eA3jIA+At+2LiAPdf3tBC066E/IKKWM57VCwva3gwcPJnjWQKcGmXKQJrlLly5yYMi+gyfY8+bNo/PnzsuMUR9//LHtDzqJRtC/f39atmxZErXGzTACjMCpU6cob9681KlTJ1q/fr1BQKCFA0Hru3fvGjzPBxkBW0MA4VEw9rSxtTvH/VUIpBLMI1OPCg3eWhSBwOdh1Oa3k3Qr4IVs5+hPtQkeCKYYPGPSOaaOkaYbdWAmBzwPJXcRthImCJzw8EjK4BSTl3zyMlyEaYVT1oxpyCWdg2z+rsgEVHVUlEBfAXdn2vhlRcrpksaUrnHZFIrA4VvP6KM/TknNJBeRav7a73VNRoLntcmQJdkFEIXFwqZ+/foyJbK2YSx6kJUFQsTQs0HqZHjiDBo0SFuM9+NAACmp79+/T4sXL6Y6derEUZJPMQKMgDkRQLYvaHAh05ih7ywQ069fvyZkxWNjBGwdgZcvX8r/4W3atKHp06fb+nC4/ykMAYS1shBxCrvpyTncHIIEOfR9NboV+IqO33pKLulNn37IDhWbIVwll2uUzgi8c/AyZFkyOBJeWkNfkA68SsHMVEBPxFhbjvcZAX0EqhdwpWtT6tGF+yF01S+KkNQvE997ntfxIZR855EJauDAgTE60LZtW3r8+LH0EsFT6wULFhDEibt27RqjLB8wjMCGDRvo2ZMnlDVrVtqxYweTNoZh4qOMgEUQ+OeffyTR/PPPP0vidPz48dHagTA2vvumTJlCrNEVDRp+Y4MIhIdHJTVhTxsbvHncZYkAe9rwRGAEGAFGgBFgBExAAJmiQNggTAphBLBmzZpRtWrVaOzYsSbUlLKLDhgwgEJCQsjX15dCQ0Pp8OHDKRsQHj0jkAwI4PvszJkzMgR08uTJBFFsZSChr1y5Qps3b5YCxuo4bxkBW0Pg2bNnVKpUKWrXrh1NnTrV1rrP/U3hCMDTJmbsSAoHhYfPCDACjAAjwAjEhgAWOIGBgbRu3TodYbNmzRq6efMmdevWLbbL+LgeAgg7Q/Yt/Ih2cXGRZ+Ftw8YIMAJJi8CWLVuoXLlytGvXLurYsaMkaVQP4Gnz6NEj+vPPP9Uh3jICNokAa9rY5G3jTmsQYNJGAwbvMgKMACPACDACsSEAwgYaNtB4yJUrl67YqlWrCOcKFy6sO8Y7cSMAnaAyZcqQh0dUZkFo2yBcg40RYASSHgFF3FwS2aXat29P//77r+xE3bp1pWAxxMJPnz6d9B3jFhkBMyHAKb/NBCRXk2wIMGmTbNBzw4wAI8AIMAK2ggDEC/39/Wnr1q3k7v4+s9x///1HR48eZS0bE2/k7t27qUGDBhQWFiavbNy4Me3Zs8fEWrg4I8AImAsBRdy8ePGChg4dKgXWUfegwYPI1dWVs7yZC2iuJ1kQYE+bZIGdGzUjAkzamBFMrooRYAQYAUbA/hBAyICPjw/9/fffBGFirWGhgywslSpV0h7m/TgQQIr0Y8eOyUxbSKMOa9KkCaVNm1aGTMVxKZ9iBBgBMyHw448/UsmSJWnYsGHyc4fsOoq4gfDw5cuX6ZdffqFCBQtRr169aNOmTTLblJma52oYgSRFQCVLZiHiJIWdGzMjAkzamBFMrooRYAQYAUbAvhDo3Lkz3bhxQ4bu5MiRI8bggoODZfhAjBN8IFYEIGoK0ebixYuTIm1QGOQXPJnYGAFGwPIIjB49mmbOnElp0qQh7FepUoUGDx4svWwQqvjJJ58QCFaQO59//rkUIl66dKnlO8YtMAIWQIA9bSwAKleZpAgwaZOkcHNjjAAjwAgwAraCwEcffUTQePD29pZpqQ31G4ue5s2bGzrFx2JBAIKnTZs2lWdVGla8adW6lUz9HctlfJgRYATMjECdOnWkN82+ffvoq6++oqCgIOrdu7fM5vbpp59Sv379pHfhpEmTpND6hg0bZLY3M3eDq2MELI4Aa9pYHGJuwMIIMGljYYC5ekaAEWAEGAHbQ6BPnz509uxZOnDgAGXOnNn2BmDFPcaP59atW8seaj1tqlapKrNJXbx40Yp7z11jBOwPAaT57tmzJ61YsYK2bdtGFStWJKRIBnEN4fVs2bLRuXPnyM3NjdauXWt/APCI7B4BJm3s/hbb/QCZtLH7W8wDZAQYAUaAETAFATxdhrjwoUOHpACnKddy2fgR2LlzJ6lQM62nDa5E6BR0NtgYAUYgeRBAVjeES+3du1d630CAfcKECTJM9Pnz54RseWyMgK0hwJo2tnbHuL/6CDBpo48Iv2cEGAFGgBGwOQTg3j9y5EgaP3483blzJ8H9HzRoECEjFIRyXVxcElwPX8gIMAKMgC0jkD59evr4449lqu9OnTrR/fv3KSAgQIZH4buWjRGwJQRY08aW7hb31RACjoYO8jFGwJoR+GHTDdm9a/dfUhGPDHK/WC5n+rCKmzV3m/vGCDACFkJg4MCB8qlw/Zo1ZYaTJUuW0MqVK6lcuXImtQixTQhwIiwKCxY2RoARYAQYAaJff/2VihYtSuvXr6cHDx5IL0TGhRGwJQQ4PMqW7hb31RACTNoYQoWPWSUCy088pk0nH9LxSw8oIjKSsjpnIN+QVJTdJT1tPXWHvl5yiWqWzkHtKman9hXdrXIM3ClGgBEwLwIgbI4dOUKrvxtLZfPmoVP16tIXM2dRly5d6Ig4bqweDTxsIJALPRUnJyfzdpJrYwQYAUbAxhFANilY4cKFqW7dujY+Gu5+SkOAPW1S2h23v/EyaWN/99SuRhT4PIxm7fenrSce0l2/J1S5TF7q1bo8eeZ01htnAbru84wu3nxAXy2+QvP2+tEf3YpR0dxRnjh6hfktI8AI2AECIGyuXLhAe37+mTJnyiRHVCF/Adr43XfUQ2Q7ademDW3YtCle4gb1gLC5evWqTH9rB9DwEBgBRoARMDsCirgxe8VcISNgYQSUpo2Fm+HqGQGLIcCaNhaDlitOLALLDvtRgx+P0p+br1KObJloyEc1qW2dwgYIm6iWCudxpbb1ilDfDlXocUgEtfr9BG06HZjYbvD1jECyIHD70Svac/kJIRzw8PWnydIHa2306dOn1KpVK7oispls/PZbHWGj+gsCZ8u4cVTU3Z0aNmhAZ86cUadibLEI2bFjBxM2MZDhA4wAI8AIMAKMgH0gwJ429nEfU/Io2NMmJd99Kx57n7kX6O8T/pQrpwv1/7A05YvhWRN753NmSUd92lak1bsu0IBZZ+hs80L0XZv8sV/AZxiBZETgrE8w7boQRH+fCaTwt5EU+PQlPXkRGq1HM7bfpqolclGbSjnp45o5o51LaW9A2LRt25bobThtHDMmBmGjxWPWZ5/RwKlTqWvnzvTzxIlR12kKQGQTGjYQLk6dOmHPMJ48eSKvdXV11dRs3l1ksQoODqYPPvjAvBVzbYwAI8AImBmBN2/eyHTh2bNnp1SpUhG+IzMJIj1NmjRmbomrYwSMR0BlKsScZGMEbBEBJm1s8a7ZeZ9XH3sgCZsyxXJR50YlEjTaNKkj6aOmJWnrgfS0aO9d6l7NnQq42a+waLhYwAa/CI43DCRBYPJFFkHg5ZsI+nrVNVp9wIc8c2WhAp5ZqUDuLJQhnQNlzOBEmTKkJcdUkbLtU9cD6cqth/TDqou0+ogfrR1cmjKJcinN4DEDrRq3bNlok0hJq0Ki4sJhlhAXBnHzf//3f3Tv3j2C2DDsxx9/pOPHj8tjcV0f17nQ0FBCelwsTk6fPh1X0USd8/b2pocPHzJpkygU+WJGgBFICgR2795NCDldsWIF1apVS5Llv//+O1WsWDEpmuc2GAGDCLCnjUFY+KANIZCwR4s2NEDuquUQCAwMJB8fH7M3MHvPPSqYJ3uCCRtth1rWKkA5srvSLzt8tYftah9iq+XKlqM2Qr8jue3Zs2c0YcIE6RWQ3H2x5vaP335O3WaeoyM3n1PvNpXo0w4VqElVL0KIn0eOTJQ5YxodYYNxVCicg7o1KUn9OlSiB09DqfkfZ8j/WXRvHGserzn6Bm8YEDbugiAxlrBR7YK4aVGtmsyAsmjRIvrjjz9ktqnz58+rIgnaHj58mEqVKiWvvXLlitzCEwgpwy9dukSbN2+mly9fyuMIwcI5ZXgfFBSk3hrcQhR5+/btFBYWFu08nlyjbnjfaA3H161bRydOnNAdxlNvkD7Lli+jVatWSfJHd5J3GAFGgBGwEAKLFy82WDM8By1JchtslA+meASUpg172qT4qWCzADBpY7O3Lvk7vmbNGmrSpIl8jR8/XqbJTWyvFuz3o4u3n1KHBHrYGGq/XgUv2nbUh877hhg6bfPHcB+g77Fv374kG0v16tVJLVK1jQYEBMiFIQg9NsMIwJNsgAj/e+uYngYIEgZEjbHmnjUj9WhRjt6INXx7QdxcfxBFCBh7va2WA9nRu3dvioyIoIVDhxrlYaM/VkXcjBYeOvXr16d///1Xv4jJ71EHsqjgtWfPHnn99evXCTo5n376KU2ePJnatWsnj8+ZM4fUIubWrVvUr18/GToQW6MTRTgXrl24cCHhM64Mgsnw7pk5cyZVqFBBt/iZNWuWPA5iplu3boT3sO+EKPOwYcNoz797JHkDYpWNEWAEGAFLIoDvJpAz8BDUGn6rDBXf4T169KD+/ftrT/E+I2BRBNjTxqLwcuVJgIDdhkfZU7gInp4iba21scNIkdupUyfatm0bwR127ty5VLx4cWrUqBE1a9aMSpQwPbRp2X5falK7KLkKTwNzGRbFFUvloem779LsPqb3KaH98PX1JU9Pz4RebvR1WNACd63h6T6e5FeqVEk+8W/YsCFhoajCOfz8/KSOR40aNeRleJKPBWjBggVlOk9V16lTpwiLUHyeypcrT+5C2BWePRgbnt6jzvz588v2EWoCsgYEXq5cuVQVcos6bt++TQ0aNiBHh6ivHfQP/YZXAFyo3dzcol1jj28eBYfR5/PPUYcmZaT3TELGiM9Gz5ZlacXOi9Rz5nk6PK5qQqqxmWswv6FhE3D/Pq0a/S15JWKeyFApMfIuQuNmx86d5OXllSgcoIfz22+/EcjKpUuXEr4TYa9evaK1a9cSnuwhJODRo0fUvXt36emD8Cx81qpWrUpZsmSJtf2NGzfK71QQQr169dKV27BhgySERo0aJUO94HFTvnx5WrBgAc2ePZuaN29ON27eILecUZ8nkLn16tWTRBK+k63t/4huYClwB/MDr4jICDn6yIio9/KYICi1WxRQ5bVbLETwXnteHVNb/fPqerSr2lTX61/z9u3bGPWr69VW9T/ibVRf1HHtVnZQ/MExmGoH++pYXFvMW1WfoWtUHzAeQ+cNHYurPUPltcfia09bN/ZV/7V1aMvEdlyVwXmYeq+2UUeFxJe4T/rnVRlsTW1fe61qQ79+7ftvhSC81tKnT08dO3ak5cuX6w7jdwBCXJGlD9+JNWvWlNo3ltQD0zXOOykeAXznwPh/YIqfCjYLgF2SNlhU4klnNqF7kJTeB4ZmAZ5q4onoZ0IQE0JsphoWy7Vr16YMGTLQkiVLqGjRoqZWYdHyOXLkkE/A8RQcC3YQOHgSPGXKFLkgxxPtpk2bUrly5Yzqh4/ImFOvWmajyppSqHxxN9q4K3GhEKa0h1CO9u3byx+mrVu3lgu2IkWKmFKFUWWBOciQypUr68qDUGkgMubgaTyItAsiJTJckfG0HgQgFpn79++XT/1xv/AkDOVB2Ny8eZMgzvrFF1/QtGnT5AuLSxcXF8rsmpkcHBzkPUZjCAHBohP3GOQLnqqhLZAxR48epdy5c8s+/SzSMf/111+SyIGXA65Lly6d9DQoUKCA/Jziyds5kQkorkWsboBm3Dl58iTdF2QA7lFS2MiV16iyIBAR7pQYc0qTmjo1LkHz1p2gOd4+1L9ensRUZ9XXgggJECTjSkHYlCtQMNF9VRo3fXr2pA2C8AAhnhADYYnPGuY3QpAQDvXixQtZFRYs+G7EcRhInBYtWtDXX38tSU+QNiBXYrPXr1/LuhWphO999cQaC546derIS3EepCeILX9/f/kZxolCBQvpqp4xY4b8HwSvnZw5c9L69evlVlcgmXeKFSsmieE8eex3DiczxNw8I2BxBODZh7BTEDXKuvfoTt26dqO0adPKQyBtoP+F37O5PaJ+H+B7FKQzGyNgaQQUscmkjaWR5vothYBdkjYqXAQ/ppPKEC6ChSl+gGpNhYt0Fk92E0LawCsBC+G+ffsSnrDiR7+1mpbAwQIDiwM8iZ43b54kbapUqUJ169WlqlUMewbcffyanoe8oayuGcw+RM/smehZ8BupA5LL1cns9etXiMUUFmYQ4sMTc8yNwoULSy8kLN7Kli2rf0mC3g8YMEAuCFu2bKm7fu/evZKwwZN+EJjwhorLQLLBG+DPP/+UxMmXX34pSRsQTyByEOYBjx31wwvl8HR/5MiR0cYxZMgQwmIT41SGp3XLli2T5BEWmiCAQNqoLDhoC4QJPG1AJCUVeaL6B1J3p/C4gNcCvJHQPraWsEUH/Wnv+UD6v67VzVJ9RicHql3ZixbsuUOdq+Yi1/SW+zrHXBg7dqycJyAjk8qD7JtvviF/QUoeESSwMaLDxgKriBt48ODzmRDiBp9vkNGY97Bx48bJuQ1ixJA5OTlJTR54xBw8eJAgzBmbgdSEVg48GOFlA6JGEZog8UGMwgMHfUC4AfqPzxZCsECMOrs4Sw8KZGspVKiQPA7CFmVwTdeuXWNrOsmPY5GHF/R78PQdP6i1L2T1ku9Tp5J9S53q3Xu9cjiprgO5DNNdK8rC1PlUoi79eqKdF+VxbWzXa4+rOrXXq/NqizLyvF678iD/YQTsFAGQx3nz5pUPcTBEePbiM47fJfhtDHJbaYLZKQQ8LCtCQHmPqe9jK+oad4URMAoBy/3KN6p5yxSyx3ARPJ1QT3FNQU2JYWLBjR/wWDhgH4sCeSytOJbWSR7HOXUe57CPcuoYtnAJfhP6RobZINRGvbBYx1Nl9R77CKX58MMPpRcHnqbAqwNPfbHAAAkAMkdrNx++IheRMSddWstILbnnzEiX/V5SUpA2GBcWWfASwAvioHjB6wovkHxY/EJYNTEGYgZP0EHoITsO7Nq1a7on7vokomoL90sZnn5Bj6Nx48bqkBQ+HTFihPSYArHy+PFjmj9/vvT60hUyYgefRXibeQkSC/8o8QPuxo0bOtJGeY7BPRreCElt8CjCC0TSli1bZKy9h4eHJG+wkIxtAZ6Qfq4+EkBVyuYjFzOG/lUo7CY8MoLo29XXaHovy4X+4f7hBzdIhF9//VXePxBvILnww9vcdkeQRL2F914uZ2faIDRZzEnYqL4mlrgBOQqyFOGgMBAx+BzF9ZlGiBRIQXjBKU801R/9Lcgg6NF8//33cmGjSJsePXvQ6G9HSwIG90Bp5oz4agT9Nuk3WTfqgkdd+w7to4WpQmfCUqSkfv9NfV+yZElTL+HyjAAjYOUI9OrdS0fa4LcfHpDAEx7eNmPHjeU04FZ+/+ypexweZU93M2WOxe5IG3sNF0F4CkJcsAA2Jf4X2iZwCYTbqrUYCADl6q/t02W/EMrknE57yKz7Htmc6fTdZ9SgeOw6EmZtUFNZhw4dCC8s7PBk/8CBAwRSBCQWnpTDEwuEhqmGJ7kg9ECWKatStQpN+nWSnCsg7ZTBEwphS5hDWHCqf2DQHwKhBMFTkCjQtwHBB8+wn376Sb7vKUJJVq9erSNtoI2BOkqWKql7oq/a0W6x0ARxBFIJHkYIgRozZoy2iMF94OQsFuxY3CaFwQsIL6SBhqcexFzhtQBSAuF9SvsnMX259+glNSueLzFVGLy2ZqUC9PuiA/Tjh0UoswW9beDVhdfZs2dp69atNFWk0f7ll18kKYt7W0/oppjLOorPSuXChWjm4M/MVaXBekDc1Bs+XGrmeAuNJlNMq9WA6+CJpAzEKQzktzbDHj6D8FKC52R8hnnXqHEj+flCWeU9gqfXK1eulJ95EOnK4MGIzygySuGJIj4/MJCkISEh5OLqotOTUtfwlhFgBBgBcyOA/wd4wVq1bCVfqg147oK0gbeb0rdT53jLCFgSAQ6PsiS6XHdSIGB3pI29hotgwQ+vCoRHwVPDWINHy4MHD6SmDn7gK68Z5UmDrfK60Z5XHjbqmLzOKSou2ZCnDRb68N4AeaD1uIHeAogCkAcItcHT1PxCuFZpPWjHER7+lkJfR4npaY+baz/8bZRAoaoP4TiOjo6SoDC0xbEYx9NEfWRCgkOk5xMWQ/CA0k+9q9rQbjFmlIM7MLRcgAkWVMgog3uKY9C9gaeHvqifth79fXiDQFtDWb269Wjzps2yHa2nDcghLDTRPhaECLsDmQc9GWSbQTYH6GJAHBCLQng64N7B0DflyYP3yPqA0BU8zQfZAcIHZZQhDAM6OUivjDAqkD8gqEBOGUPEgDzBnDemrGrTHFvMc3hD4AWvEhA46DPGg5AS9CkhFvg8jAKDXlJ+94Tpp8TVZjZnJ8rhmp4QXpjZ03TdrLjqNnQOoX144f6DJECoHOYOdI1wzzt26hhNU8VQHXEdWyQ88JwFaWhpwkb1YeO4cdRQzFHMVa3grzpvri08t/B5gmdM6zbGaSjJRU1UpE+MbmCuGjL9MFz1HW6oLB9jBBgBRiCpEcBDITZGIKkRUA8qOTwqqZHn9syFgN2RNvYaLoLFNggYUwgbNUm0Tz3UscRsHTM4StfWuOrAU+e///5beoGAIMBTeRUKE9t1ebI7UfCr9x4jsZVL6HHfh8+pfKNc8nLMEyzSkMnAWgy6FXgh3MQU0gbhGAMHDpRifvDGwhN2CBAj3AgkWunSpeUQEY4BDRccc3B0IGT6wI8n/AODyzIW4QhnUos+6PGAZIIbs/4CEeQFvFBAWKE91KP1KNBiivARvEBagSRUpi2P0Dmt4ZyTO+jqAABAAElEQVR+Riztee0+yC/oJ8VnavGqCEntFpijf+Hh4botiEjoh+TLl48gWAx8kIYZ/cILWiFYiBtjp3yDyU14eoloRItYlizpyUd48pRNAtJGDQBeXph7eAEfeFPhPkDzqFq1avTRRx9JDxZV3tjtM18feiE8BJPKEHpVWXiYpQp4YNEmQXRCZ02r+WTRBrlyRoARYAQYAUaAEYiGAJM20eDgNzaEgN2RNvYaLoIFpTn1Ncw9RxH2cvjwYdq0aRMhnTRCbqZNnxar6LCh9nOJhWfom3AhRhxmVt0PtPUmjOjh4xAqnjtK5BiLbWMX3Ib6ao5jyPoEoWZ4dAA7ZFDAq1+/fiZVD68HXA+SQWsgIrRhU+qcjoDRe4KPf2RKNwNlQcRo36vr1Rbns2bNqt7Gu9USNnEVxlxHCFd8JB/qgEcOwpjwuYdXlNoilES9cFy7r8qh/yivNbjPwnsKYYV4Ke8xVQZEGLyRoF2CLFvGzqHTd1+ItOlR6SZVXebcZnPJQPeexE94QoQWcwLjeB36ml6/ivKOUwSfGq/aoiz2cU8wvxSWwA6Y6m8hKI1sXPgOUILW8MDCPDfKnjyl1qVK04xly2naxk30f23bGHVZYgp5nz1H3oK8nTBhQmKqifdaJb4db0EuwAgwAowAI8AIMAJmRUB52uj/7jNrI1wZI2BBBOyOtAFW9hYugi+ae/fuRUvrbME5YVLVyPiBEBkQDwghgfcFxIdNWcyrBvNmjnL3D3rx2uykje+j5yIrVbokEyFWY9LfgoyARwI8S5BBAcRKkyZNpNcPQksSY1hA61vGjBllqJMpOkj6dST1exAD0PyBVk98hkw5eFnKcJ8gUAxPEmS7gB4JyCR46UAg1hR7Fvxe/NmU64wpm9YhNT16HjdpgzFA3BZzMCkN3mPIUGYUaZElM3mJEMoJ/frS13PnU6vq1cjLzc1i3X0qPMmGz5lNE8cKsWMRvmlJQwY5YJ8jZw76qNtHlmzK6LpByiF8NiF6WkY3wgUZAUaAEWAEGIFkRoCzRyXzDeDmE41AKjGJowt9JLrK5K8AQpkIF8ETYoSLKFNPkxEuguNqUYgfrvrhIrgG0KhwESyI8aQ5tnARlMd5bbgIjsVleHodn/dB+Ntwyu+VX7rUI1Wsl8jiYi0GnJE95XMh5onUueZw+28w/hhld8tOTaqad5yrdl+iwtkd6M+exZMFPpWdCOQWhJihNQNvJIQZIfyG7T0CSE2MLBPJ5cKK7wakKT916hQ9fy5C6oT3U+XKlaXWT0Iz3Kw9HUSfzTpJI3rXFpmQ3oeIvR914vZmrjpKU3qXoJqFXOOsCOFxyvD99iJY6DG9CNbpMsG7KCGGOY2QNrzgZQOSEPMaeEFEHToupmAXGeBPnw77gq6IrHPekyYlpEtGXdNNhCs5C1JotiBULG0gbeARBw87fA8klSF8EKFZIEKV4d4rLzUQx/i8oU+2RO6qsfCWEWAEGAFGgBGIDwF4/Pbp00fqM3711VfxFefzjIBVIZAnTx6yS08bewoXgRDlzZs34yV3kmNmAWetLok5+jC4SV76ZtlVqlbKg1zNlBr50t0n5Ov/lOb1rmSOLhpdBwg8JdQKAgDeGdB2gTcSCBtzGsi96dOmy6w0KmuMOetPqrowDujIQEdnyZIlRoVImaNv8KJBRiToHCEDHUSHIZ4MbRZzWO0CUZl8HgkvMnOTNo9fhJJvYDBVzu8Sb1fr1q0bbxljC0BkHOFWIAOgDYWwO6SUHjZsWJxpr42pP5V7LpolNJVaCj2sQUIjZ8bgwcZcZlKZT/+cTn4vnpP39u0mXacK3xKE0pUrV6hI0SL07Okz6UVTp24dXUaU48ePSyK/evXqkoDEj0WEG0LzR99A+ANLiLWD/MYDBmRng/cdvjfw8ODQoUNSQBzfucAeguXwblTk5o4dO6TWEs4hTM1NkFHIYuUtsmLdvXuXtr8bJ7TR4I0KogY/YtEeBL9xPUS32RgBRoARYAQYAXtDQPkoILSbjRGwRQTskrRRN8JewkXi88ZR47WHbYdK7rT+RCCdvORDDSq/z0aUmLHtP36DBjTOR3mzWi6duH7/7ty5I71o4LmA8Cd4GtSrV89inlIQFUaYGjx3rI20MfSkXx8v9R4kJbJaISUywsiQLc2SBmFveJ8cO3ZMLpihJ4T05uY2N9c0lM01A12+EUiFcsVPrpjS/q17QeTplonSOqQy5bIElUWoE8gEEADwSEK2M5AA0PcBUYCU1ua0pSKLWTtBctYXT8U2fCdCmIRosDlsoNBBuuznT5u2bE1wdcj8BmFqjBlEH7yy4NGClLbt27eXHnWoHOchfB6bPXz4UJIzIGxA0ANLZHSDGPkP//tBpsuFwDOEwZs3by7LIGPX5cuXpWeM8trB3IV+ULZs2eT10Bm7fv06wcsPpsS+QfaAtFHZ4ODBCa9PEG5sjAAjwAgwAoyAPSKgSBvWtLHHu5syxmTXpI2hWwgiBymN2awXgaFN8lGrCUfFE2YnqlHaI1EdXfPvZcqS0YGGfJAnUfWYejHC2EA6WForAiGAyNJlKNMSnswj9AHhPcrwTwtph7FQrFGjhgwRxEIcIYJ42o7QFhBOOIeFOUJaHj16JBegCHdR4sDQ5sATfHjFKP0iU5/0Y6GIPt73uy+9E+rXr68T20boIjyVLG3ArmHDhlLrBR4RlrTqRbPQ/ksP6INq+ckpTWqzNXXyoi/1qJXbbPUZqghzApmPDh48KEP7kBFOZdIyVN5cx+ANskEQFLhHg/+cQctGJt6leYAgKbwFSbdz507pbZLQvkKjByGiMgvd2TO0a+cuqXt04+YN6eGiwpGqVKkixatz5YrKXKffHvR+KlasKD1wQLQoAWcQvdu2bpOkDT6LKt08vG8QrgdPH2TogjaW8gjDtfDkA4G2f/9+uQ8dpk6dOhn08MFnH5n+ELpljvBW/bHxe0aAEWAEGAFGwBoQUKSN8k61hj5xHxgBUxBIcaSNKeBw2eRBAGEeI9oVpl83XKFnIvSjWQ3TPW6u3HtKW70vUZ7s6Wh235LJMhBLEzbaJ/T6WiStWrWSAqM4DgIGGZZAgmDxC9dQLIaxWMNT+DVr1hB0ZOCpg4Xe4sWLpfjugAEDqFSpUtLzBQs/EEDImoQwDngTlCtXjkaMGEHz58+X5I2pT/q/E54TWDjjCT+8ybBVGdKggwJPDpBDltTZgMdCUtkQEfr3z6kAOnftAVUuaXgBb2pfVu66RDmEgPcQQXRa0pBRC/G0ICKTenGPuYo5htCdgTNm0KxBgxI0VIgOfy3murcgCletWmU2rzeEicJDDF4wMJCXmLcqBBKeL76+vhQbaQNvGHyuGjdurBsXwqWQmQxeeiBSQbROmzZNngcxg88pvl9AfCJblzJFquIzgxArZdp9dQxbeAiB+EHYJhsjwAgwAowAI2DvCLCnjb3fYfsdH5M29ntvbXpkXzb1olKeGenb5Vdpw+tQqlk+P+XMEn940+s3EXTwnC/tOXKdmlXKTbM/LpEkYSPJATYIF3i/IM26EoVGP7AIhMYInqLDSwaeZVhEgiDJJMJLQLrA8FQ/vlAq6Lrgyf0kIQYLEge2bt06GX6BMKK5c+fK9uFxAzPlST/CkhAy9sknn1CJEiV02hyoB14FCClBeBRCmOzBSntmok61PenoLfOQNmeuB9KN24E09/8qWhweeFyZIiRs7g6BIARx0aZNG/pUzIeZgjQ0xUDYtBOeQn4iUxIIG9RnLtMnFaEbBIITpChIUpgK1QXxhZTxIFDx1A/kJMgdfKYWLlwoPdlA2KA8PhMgyEaOHCm1a0D6gISFThY+w9CswecnPDw8zqEooXPlkQPdKJBMMPSnTp06cV7PJxkBRoARYAQYAVtHQHnaIEMpGyNgiwiYz0ffFkfPfbZqBJqUykHrh1eiTI5vacqygzRv41nyPu1DD5/ETJ183ecZrdt7lf5YfICOnLpNfRt70V/9StotYYMbB5FRaFjAkI1KGUgbhBdBzDe3R1TYDEIpoBUD3Qy4huIFDwF9Q7iV1vBEAgtQrTsp6p8hPB7gGYAFJDKqKTPlST/qgAcHwkBA+sBzSNny5culVoq9EDZqXJ81zksPH7+gVf9cUocStA0Ni6A1O89Ru7r5qVFR82rkJKhDSXARPG5AUF4SniUgbow1Q4QNiBNkT0qMDRbiyFOnTpVeafBIU4bPC8jOKVOmyM8nPqPqxyJCEPPnzy8JGZBgERERkpDp1q0b9ejRQ5Io2CpDWBPCEJGdD4bPNDxjkLUPnz983oYPH05Hjx1Vl8TYAjeIIKMukDRzZs/RlYGnGbRz2BgBRoARYAQYAXtGAP9vYdrfs/Y8Xh6b/SHAdKP93VO7GhHEg1cOLkOn7r6gZYf9adcpH9p98JrBMVYolJWGtS5EH1Z2oxwuaQyWsaeD0MoYM2aMDG2C5ogyhEPBwwZP1pEVCYtILCoRItG7d2+5wKtapSqBoEFmGgilwosB3jgQmVX/2FR9+luEgaA8FqwIZ4JnQFwW25P+QoUKybAthGYhCw68B5C1CYawDxUqFVfdtnYuX7Z09Eev4jRs0WXa/N91al2nsMlDuB0QTPPWHqVuTUvQ7+0Sp/lkcuPJfIEibuDBYqzHzUjhWaPvYfNAeNwsXbpUkhi9evVK0KiQBcpQJihUhtAmvPCZwmdM/UiEB82WLVvkcRAwyk171KhR9M0338gsUfCGU4YwRLy0NmHCBBo3bpzM1qW8bFCvNpOfEh1W1/3www/Saw3aVVrPIIgRKw86VZa3jAAjwAgwAoyAvSGgHp6o/7v2Nj4ej/0jwKSN/d9juxhhhXzOhBd1KUJ7Lj+JMabSHplSBFGjHTjCnhDmgaf32if9WNhiEYiwIywMx44bK8MtIFbavXt3GjhgoCR1EG6BrEnQv4FnC+qAwCw8ciZPnqxtKto+nvQjhTDETkEEQdfm888/j1ZG+0b7pB/HsTjtP6C/9DZQ5aBno0JJQBrdu3dPiq2q8/a0bVYmJ+UeloGGLrwkdJeuUMlinpTf/f1CPa6xHr7gJ665TANbFqdxrVIWYaNwwXxCOFEvEZ73qSBOZsaRDhznTwjPMP2QKHxu4GmD9OQIUYK3lyVMS5Bo6zd0HMQOUoIbYyCCYCrsyphr8F2Al76lpOyE+mPn94wAI8AIMAIpA4GIyChPGyZtUsb9tsdRphLMY6Q9DozHxAikFARAnMCbBt4p2gUYPGBSpU6l069QeIAUefr0qdSz0S768BTewdGBkD4cx5V3gLpOf4v6ERpl7EITehzqST/+aWIf4VEuri66PkJvI79XfqnlgVTEXiILl73ao5Bw+mzhRfI+95DKFnGnepULxqrbhPC/I+d96IrQwxnVsQR93jhlEjbauYA53F2EFeURnikzhceI1hAShWxTvs+f0SYRAgSix5BBNwnETXzEo6Fr+RgjwAgwAowAI8AI2AYCCAdGWDM8Vfv27WsbneZeMgLvEEB4O3va8HRgBGwcARA2MC1hg/daQgbvlYEwUWm61TFsnZycot46aI/Gvo/6jSVsUIv+k360p2vzXTMQSL1582aMscTeC9s9kz2jowj9K0uHrz+l1ccD6K+NxymvmzM5Z0hHGTOlo7C3RA4USf5BwRT87CU1Ku9G0z8qIAS6jfPKsV1kjOs5iJitIhX2QKEBU0/oukweJDKaFShIGw8fpPHLV1IG4UETF2GDVhDCBANx8/z5cxo9erR8z38YAUaAEWAEGAFGwH4QUD4K7GljP/c0pY2ESZuUdsd5vIyAlSOgTz5ZeXcT3b3qhTMTXsObetGOC0F0/7HwSBIeR35P3tCDp6H0eSNPalfBLdHt2GsFs5YsoQFCC6nV6DGUURCYIcLzDOFPS8Tx2DxstFhoiRscZ+JGiw7vMwKMACPACDACto+AIm3i8yK3/ZHyCOwVASZt7PXO8rgYAUbAphDwEKLbfetEZfuyqY5bQWdnr1hBd86epWXC/bmWSGGNtNumGBM3pqDFZRkBRoARYAQYAdtEgD1tbPO+ca+Jw6N4EjACjAAjwAjYPgJeIoX9twbS2Bs7MhA3CPn77LPP5CXscWMsclyOEWAEGAFGgBGwbgSUpw2TNtZ9n7h3sSPAnjaxY8NnGAFGgBFgBFIQAm3atKGMGTNSnz595KiZuElBN5+HyggwAowAI2C3CCjSxtGRl752e5PtfGA8c+38BvPwGAFGgBFgBIxHoFGjRrR69Wr68MMP5UVM3BiPHZdkBBgBRoARYASsEQFF2rCmjTXeHe6TMQgwaWMMSlyGEWAEGAFGIMUgUL16ddq6dSu1bNlSjpmJmxRz63mgjAAjwAgwAnaIgCJtODzKDm9uChlS6hQyTh4mI8AIMAKMACNgNAJlhT7OXu+9NHv2bBo3blys120W4sdsjAAjwAgwAowAI2C9CDBpY733hntmHAJM2hiHE5diBBgBRoARSGEIFCpYiI4fP07z58+nESNGGBz93Llz6X//+5/Bc3yQEWAEGAFGgBFgBJIfgYiICNkJ9rRJ/nvBPUgYAkzaJAw3vooRYAQYAUYgBSDg7u5OFy9epJUrV9KgQYNijBiEzbx58yg0NDTGOT7ACDACjAAjwAgwAsmPgPK0YU2b5L8X3IOEIcCkTcJws8mrsKgIDAyUr7CwMIuNYerUqXT16lWL1R9bxeFvw2ny5Mn04sWL2IrwcUaAEWAETEbAxcWFbt+5LXVuevfuHe36cuXKUfPmzenHH3+MdpzfMAKMACPACDACjIB1IcCeNtZ1P7g3xiPApI3xWNl8yS1btlCFChXkq2TJkjR8+PB4xzR06FBaunRpvOVUARAn69ato0uXLqlDSbaNeBtBv/32Gz19+jTJ2jS2IeBRq1YtY4tzOUaAEbAyBBwdHOnW7Vu0b98+6tSpU7Teff/997R8+XJ6/vx5tOP8hhFgBBgBRoARYASSHwHlacMpv5P/XnAPEoYAkzYJw81mr6pZsyb5+PjQ7t27CQKap0+f1o0F2g2HDh0ifLHhtX37drp8+TKdOHFC7h84cECWRTmce/jwIe3atYvOnTsnj9+4eYN27dxFI0eOpKpVq+rqxc6zZ89o06ZNFBQUJI/7+vrKxY8qdOvWLdm2en/37l3ZhjEhB69fv6YNGzYQ6tA3jEc7RpzH2I4cOSLH/+jRI3kJPIPUOPz8/HR9wTjRl5MnT8qxaj2I9MeEio4eO0rAAdgq4uratWvk7e0t60F9CjuUf/PmjTy3bPkyWrVqlcQUx9kYAUbA+hAAcYPvgLNnz1KrVq10HcyZMyf17NmTfv75Z90x3mEEGAFGgBFgBBgB60AgIpI1bazjTnAvEoqAY0Iv5OtsG4G8efNSxowZyd/fn8qXL0/t27enx48fy0HlyJGDVq9eTdu2baP79+8TxLsQTuXh4SG9RUDi/Pnnn1SsWDF5/NSpU5KkePDgAcGb599//6WffvqJOnbsKOvD+08//ZQQRgAxT4h6Qieie/fuhGvR3vjx48nT05Nq1KhBv/zyCy1evJiKFClCX331lSRQEJ5gyEAcNWjQgAoWLEgvX76MVgSLKvQJx1HvnDlzZOhUw4YNycHBgTJnzkxjx46l//77j9asWUNPnjyRnjr79++X7WMsAwYMoFKlStGFCxdk/9KnT0979uyRY9QfU+3atem3Sb/RnTt3CPgePXpUjhXYoQ0YMIUBSyz0vvvuO9q5c6f0fkqbNq3c4jgbI8AIWCcC+JyCtIHXIr5j8D0B++abb6hMmTL0xRdfyO806+w994oRYAQYAUaAEUh5CERGRMpBs6ZNyrv39jJi9rSxlztp5DhAPvTq1UuSGGnSpJFbeIbAGwSeMHjB4wRkB4gZpL3t0KGD3B81apRsBWQFCJUsWbLI8l9++SWFhIRQ06ZNaebMmZJA0XYH4VIIs1qwYIEkb9BG4cKFqUSJEtLrBJ4y8NgBcQQvGBA2ixYtklsQJv/884+2umj7IEPy5csn+zFp0iTduevXr9OZM2ekFws8W/7++2/p7YNtpkyZJBGE4wsXLiRnZ2fddYZ2QEDBUD9IIpihMckT4g9IobVr19JHH31Ee/fupRYtWsgFHQgfYKpwRXmEWtSrV08u9GbMmCFxUfXwlhFgBKwTAXyWQcrevn1b53EDMgef+enTp1tnp7lXjAAjwAgwAoxACkUA6wsYa9qk0AlgB8Nm0sYObqIpQ4AXR7t27QiLDiww4G1y4/oNSWg0a9aM8MqWLRshfCk+q1ixoiwCQiYuvRYQKCAkGjduLD14goOD5XXQhcBTam8ROgQvGxBECDnCC8QQyiM0CeLJsRnIpgIFCsjT8PxRhjazZ89OGTJkoNweueVhhE+BtIJXDph2vNCmvoFE0hq+4IGXlp2PbUy4DoQWDN5Br169kvv4o91XB4ELCC/cE3jqKFJInectI8AIJC8C+Ex26dKFunXrJsll1Rt8vhEyinBTFSoFQhyErjHfn6oe3jICjAAjwAgwAoyAZRFg0say+HLtlkeASRvLY2xVLYC0adu2LQ0ZMoTmzp0rQ4fq1q0rSQkVKgTvlcqVK8t+g4A4fPiwLKefcQoeK8YYMqvAYwYeNqhr2rRp8rI2bdrIp9XwrIE3DwwkEvRwQGIgrGr/gf3Ur18/ec7QnypVqkjNHYQ2KQ0ZlEM4FPRqoF2zdctWOT70Ad5A8G6B9gxMETQI0QIBBMII3jEICYvLYhtTbNfAGwiG/sAg2AwrVKiQDNvCU3ssDjFmNkaAEbAeBPCdOWbMGEnGjh49mvCd8/vvv0sNLXxfwRMQYaYgbhAWCeIbHnxsjAAjwAgwAowAI2AdCCjSBvIIbIyALSLApI0t3jUz9LlFyxYyLAiLC3iRIARoypQp0msFnivqy61z585SjLho0aI6jxZo4ECMGBoO8IhRBg2bPHnySG+WYcOG6TxO4NEDDRt44+A8QqhgIErq1KlD0JBBaJQy1AuPGLSZ3yt/DCFhVQ5bCCtDKwdaEip8C8exmML7Tz75RI5t7LixhHCwatWqSS2dgQMGyr6gfRgWXMj8AmIH5dA+0ofHZrGNKbby6E+fPn1k1hlgMGf2HEncIEQM79H/4sWLy9Cq2Org44wAI5A8CCDb3rhx46RXIL7z8J0Fsrtv375SrwpaVfAIxPdIx04dpTA6Z5JKnnvFrTICjAAjwAgwAvoIqHWN1mtevwy/ZwSsGYFUYhJHBflZcy+5b0mGADxN0qVLR05OTro2MUXgyYLjCDdKqMFTB6FR0MIxxiAg/Pbt23g1Z1AXQo9APiEbE7QllKHNVKlTEbK+aA2eNEgNDj0bkDTKkK3KwdGBkD4cx+P7ck/ImNCGq6urjKvFPsKjXFxdYvRR9Ym3jAAjYH0IIKxz/fr1tGPHDkm8wvtu48aNkjDG9ye8+kAaszECjAAjwAgwAoxA8iKwUDykhtfs8uXLpRxB8vaGW2cETEMAD/iZtDENMy7NCDACjAAjwAjoEIDuFn4Erly5UpKxIKYV8Y1QSzZGgBFgBBgBRoARSF4E/vrrL5mxddWqVVJCIXl7w60zAqYhANKGw6NMw4xLMwKMACPACDACOgSgV4WQTuhRQS8MpA30qSB8DlFiNkaAEWAEGAFGgBFIXgRUYAlnj0re+8CtJxwBJm0Sjh1fyQgwAowAI8AISASQre6LL76gM2fOELTAEII5YsQIRocRYAQYAUaAEWAEkhkBRdpAMoGNEbBFBJi0scW7xn1mBBgBRoARsEoEoPs1fvx42rlzJyEtOLLVsTECjAAjwAgwAoxA8iGgssI6pObsUcl3F7jlxCAQXZ01MTXxtYwAI8AIMAKMACMgEUBmOHjdsDECjAAjwAgwAoxA8iKgPG04PCp57wO3nnAE2NMm4djxlVaCQKdOnaykJ9wNRoARYAQYAUaAEWAEGAFGgBGwJgSYtLGmu8F9SQgCTNokBDW+xmoQ2L17Nx05coSQfpeNEWAEGAFGgBFgBBgBRoARYAQYAS0CirRJlYo1bbS48L7tIMCkje3cK+6pAQQ2bNggj+7Zs8fAWT7ECDACjID1I/DmzRsKDAwk9aPyyZMnFBYWZv0d5x4yAowAI8AIMAI2hICjIyuD2NDt4q5qEGDSRgMG79oWAj4+PjLNLnoN0U82RoARYARsEQF4DFaoUIEOHjwou4/U4efOnbPFoXCfGQFGgBFgBBgBq0NAPRRhTRuruzXcISMRYNLGSKC4mPUhsG7dOipYsKDsmJ+fH+3YscP6Osk9YgQYAUbASAQWL15ssOShQ4fo9OnTBs/xQUaAEWAEGAFGgBGIGwEmbeLGh89aPwJM2lj/PeIexoLA1q1bqUmTJvJs3rx56Z9//omlJB9mBBgBRsC6EYCnDciZhw8fRutoq1ataOjQodSjRw/q379/tHP8hhFgBBgBRoARYASMR4A1bYzHiktaFwJM2ljX/eDeGInArl27KDg4mFq0bCGvKFeuHP3333+sA2EkflyMEWAErAuB9OnTU8eOHWn58uW6jl2/fl2mDYfQ+vbt2+nvv/+mZ8+e6c7zDiPACDACjAAjwAjEj8Dbt29lodQOvPSNHy0uYY0I8My1xrvCfYoXgW3btlGtWrUo7E2UWGejRo3I39+ftmzZEu+1XIARYAQYAWtEoHuP7pK0UT8uQdpkz56dMmTIQLk9cssu37p1yxq7zn1iBBgBRoARYASsFgEVHuWQ2sFq+8gdYwTiQoAltONCh89ZJQJBQUFSgHjSpEkUGhoq+9iuXTuaNWuW1LVp3769VfabO8UIMAKMQFwIFCpYiBDqefToUVmsRo0a9OjRIzpy5AgFBAQQvHFKlSoVVxUmnQuLIOo95zy9eB1OmdM5UlbnNOSczoFaV3CjyvldTKqLCzMCjAAjwAgwAtaKgCJtWIjYWu8Q9ys+BJi0iQ8hPm91CCBTlIuLCzVt2pSOHz+u61+3bt1o4sSJBFIna9asuuO8wwgwAoyArSDQq3cvHWmTOXNmGjVqFH3yySfS22bsuLGUJk2aRA1l54VAOu8TQmd9gsn30Su6fPcppXNypCyuwpsneya6fCuA5u66Qy2reFCXau7UqCR/lyYKcL6YEWAEGAFGINkRUKQNa9ok+63gDiQQgVRiEkcm8Fq+jBFIFgSwgPHw8KDvv/9epsjt0qULIf03rFKlSvTll19S165dk6Vv3CgjwAgwAuZGICwsjFKlTkWODgl/zrL62AOat/cenbv1VHavSP6cVKG4B3nmzERZMqXVdfnS3SfC4yYdnb3+gP47fpMqFclGE7sUledLemTUleMdRoARYAQYAUbAVhCYPHky/fbbb3T48GHy9PS0lW5zPxkBiUCePHko4b8AGURGIJkQaNmyJUF4GIbFjNa+/vprypEjh/YQ7zMCjAAjYNMIJNa7BoTN5/PPUcb0TlSldF4qW8ydvNycDWJSIl8Wedy9qhe5ZExLO/ddod6zzsljCweWISZuDMLGBxkBRoARYASsGAHlo+DoyEtfK75N3LU4EOCZGwc4fMo6EWjbtq2uY9C0cXB4LyqG7CtsjAAjwAgwAlEIbD4TKAmbosKzplnNIpQjs5PR0FQvlZtKF3ajP1celtf0mHGWlgwqy8SN0QhyQUaAEWAEGAFrQCAiQoi4CWNNG2u4G9yHhCDA2aMSghpfYzUIvAl7k2iNB6sZDHeEEWAEGAEzIhD4PIzGrblOH7WqQD1blDaJsFHdyOTkQCN71ZKvV28ipNfNg2fRPRxVWd4yAowAI8AIMALWiIDytGFNG2u8O9wnYxBg0sYYlLiM1SKAlN/s6mi1t4c7xggwAsmIwJx998nTPQupkKfEdqV3u4rk8zCYlhy6n9iq+HpGgBFgBBgBRiDJEFCkjdY7P8ka54YYATMgwKSNGUDkKpIPgTdv3jBpk3zwc8uMACNgpQj4PwulaVuvU5t6USLC5uhmdmcnal2/BK086G+O6rgORoARYAQYAUYgSRBQpA2HRyUJ3NyIBRBg0sYCoHKVSYcASJu0ad9nPkm6lrklRoARYASsF4GZe/2E4LCH2TtYtWQuikjtSEuPPDB73VwhI8AIMAKMACNgCQSYtLEEqlxnUiLApM3/t3cnYFIV5xrHPxj2XdlXAS/IqogCgiIKArIIgkEQZIlPQNF4VYzRRA0qItxIROINSDAajCirImqCCwRENlFyWURARDYH3FhHFmWGy1tY7ZmmZ+3uoUf/9Tw9p845VXXq/BqT4aOWvNTmWTEX0O5RTI+KOSsNIoBAPhdYuPZLa964Wlze4vyTu0/NWJYcl7ZpFAEEEEAAgVgL+KANa9rEWpb28kqAoE1eSfOcuAgoaBPtdrhx6RiNIoAAAmdQIK1AklUsUzwuPahVqYyt27Y/Lm3TKAIIIIAAAvESYE2beMnSbrwFCNrEW5j24yqgLb8ZaRNXYhpHAIF8JvD53qO2+4tDVrZk4bj0vPrJoM3RY8dt9fZDcWmfRhFAAAEEEIilgB9pQ9Amlqq0lZcCBG3yUptnxVxAI21Y0ybmrDSIAAL5WODDnYetdOlicXuDQgVOWMWzStr+w2z9HTdkGkYAAQQQiJmAD9qwEHHMSGkojwUK5fHzeBwCMRVgTZuYctIYAgj8BAQKFywQ97dITU2L+zN4AAI/VwH/F0wd006c+m/tRNoJK3Dyv20dg/dlpPPwa2lpaaFr3jFSueA11QlvL6u6wTrBulo7JNinYF7l9F56F18neAzmg/3zbeh+amqqDqFnBPO+XKS6upZb02B77uE/PD8jg/A++X4F6/pr/piRi+5nZaoyvp3wY7AvmT0jWC6YD7YXzPsywWu+n8FrKqcU/r2pTLBcRnXDy6mt4DWdKwWv+Xbvv//+0DP03xAJgfwoQNAmP35r9Dkk0KVLF9u/f7/Nnj3bNm7cGLpOBoG8FtAvGj75vD/qus/r6H+RCF7Lbt2MyqnN7LYXaXhwRnWD7Ya/R3hfsrrvnxFs01/Lsm7gF62CBU4NEg3WzW1fsnqubzfDcj/0y/cpWC6Yj3Vfg/9a6Ntu166d627d8kXt4LdHXT4eP/alfGfHvj9u7Rue7ZpfvHix+zPt/5Ko7zf48X0IXlPe/8XBX8+onL/vj3pfnw9vw1/XMVgueD2YD3+mzvWXimAZ5X0Kvx48VxlfNng9PB+rtvzzsnpmsFxGZcPLRFMuo/cLf0b4uXfy1zNqx5fzffTl/fXw85yUC/8zE6kttefLFdCf45OF/LP9UfV88tf8Udd9Xkd/7jI/3PN5jgggEJ1A+/bt7emnnzYFbnRMKpgUXYPURuAMCRC0OUPwPDY2As2aNbM5c+bYXXfdFZsGaQUBBBDIhwL6xdQHbc6rVsKtOXPw5PSlMiViv67N1uT9VrtKaXv00Udt8uTJIS39RTZSCr/uz/UXVp9XvWA+0rlvO7NywXvBvK8b6RheLnieUV7tBOeXnwi8e7BOZu8YLKf2gufBvO4FU/BeeN4HAVQ+/J5vI3g9vJwvE349ozqZvV9mbYS3l5Pn+ncMthEMYubkucE2MsqHv2NGfY3Vc/37xaq9zPobvBfMZ2QRvB5N//w7xqo93/eM2gv/DjMql9k7ZXYvUnuR3jFSuaz6ntlzg/fy2zvmxiK371ihQgXHPH78+HT/u+jtOSKQHwRO/iPBD2H+/NBb+ohAJgLBP8o+r3+F9Sk4tDh0LZN/5fJtqKzP+yGwwWu65//Px5fzx/ByOlfy9yO15++rTV/OXwseg/lgOd9m8JrP65jTvnpDP4w50nNz21e1peSHyyofTV+DddWWUnj//fBy3fPlg/lI17K6rzrhrv57yE5dlVHyz/bHoKu/FqmcroW/Z7BcMB+pr1m17e/7Y3h7Os+or5n1K7P2/DPCXbNTx9fVUcnX8ceM+urvn6p16qf/sxm85/P+6J8RbNdfCx4zyoe3EywXbDOzcqqjf0n06dw7F9kN3ZpbnSql/KWYHecu2mznlDP734ENYtYmDSGAAAIIIIAAAgicLlCzZk1jpM3pLlzJpwL+L3fqvs8XDP5bqB8R6Y/59D3pNgIIIJCVwLWX1LB1m3efDNrUy6poju4f/S7Ndu3eZ0Pb1s9RPQojgAACCCCAAAII5E4gOLo3dy1QCwEEEEAAAQQSSuCGlpVs5dodpvVnYpnmL9ti9WqWth7NyseyWdpCAAEEEEAAAQQQyECAoE0GMFxGAAEEEEAgvwpcXLeMPXh9I1u+ZkfMXmHLrgO2av1OG3Z5tZi1SUMIIIAAAggggAACmQsQtMnch7sIIIBAngocTz3udkTL04fG6WH79u0LrScTp0fQbCYCt3Wobp989pUtX5+cSamsbx06etz0eW7uB9a/w7l2VeNTu0ZlXZMSCCCAAAIIIIAAAtEKELSJVpD6CCCAQIwEVqxYYc0uaGY9e/aMUYu5b+bAgQM2duxYS0lJyVUjCj61bdvWWrVqZZs2bcpVG1SKXuCl/25mry/62GYu+NgFXnLaogI+Y59Z7D43tD/Xnri+bk6boDwCCCCAAAIIIIBAFAIEbaLAoyoCCCAQS4FZs2bZNddcY4sXL45ls5m21bp1a9u4ceNpZfbs2WMzZsywr7766rR72blQKKmQrV+/3po2bWqvvPJKdqpQJg4CjauXtCWPXGbHvv3WJkxdags+2J6tpxw/UcDeen+bC/gMvupc02d8XwI22cKjEAIIIIAAAgggEEMBdo+KISZNIYAAAtEI7N+/3xo2bJiuicOHD9v8+fPt4osvtg0bNliHDh1s69atduzYMTv//PMtOTnZtm3bZm3atHH1vv/+e1uwYIGde+65Vq/ejzsHrV692j755BPTCJgLm11oVapUMY3s2bVrly1atMi1WadOHff8VatWuWDN6NGjrWrVqun6ozY+++wza9+hvSkwo6T+qd8ffPCBXXbZZVa5cuVQnQoVKtihQ4dC52TyXqBe5eL2v4Mb2e9mfmILV2yx//s42erXrmQN61Sw/6pRNtQhTYHae+CIbd6x1zZs2m21q5S2qXdcZJ0bMR0qhEQGAQQQQAABBBDIYwGCNnkMzuMQQACBSAIa0aJgSIsWLUK3FVBp3769C85MmTLFjVz5z3/+YxqRo/Vi/vSnP9mSJUvs+eeftzfeeMO+/PJLV14Bm08//dRuuukmGzFihD311FPuo6lKZcqUsXJly1lSUpKro4e9++67dtZZZ9mVV17pgi/Lli1zz1IwZuXKlVat2qmFZ8eMGWPPPfecC+Q88MADrl6xYsVs6NChVrduXStfvrzdeeedtnbtWtee2tbz1GdNtypb9scAge6R8k6gSY1S9tqIC23WB3tsyoKdtmLNNvcpU6KIlTr52Z9yzA4f/d6KFS1k3VpUtccH1rPOTSrmXQd5EgIIIIAAAggggEBEAYI2EVm4iAACCOStwM0332xHjhyx7t27hx7873//2wVsZs+e7UbF9OnTJ3QvUuatt96yiy66yP7yl7+4wMndd9/tgjYaiaNAzvDhw92InSJFirjqKjdv3jy799577YILLgg1eccdd9jRo0fTjdQ5ceKETZs2zRQ8uvzyy91aNQr2dOrUydXTs3r06OFG2iiQpLzSddddZ+r/fffdZ5MmTXLX+HHmBPpcXMX0eXv9N7b8033pOtKgamm7vuWPo6TS3eQEAQQQQAABBBBA4IwIELQ5I+w8FAEEEEgvoMBGr1693Povt99+u7u5efNmF2zRSYMGDdJX+OFMwRWfNHVp4cKF1rFjR3/JNF3qnnvusQkTJpgCK99884397W9/c4sEhwplI6OpWxotU7t2bStQoIDVqlXLtmzZEgranHfeea4VjaZR8MmnF1980QWKCNh4kcQ4dmxS3vQhIYAAAggggAACCCS2AAsRJ/b3Q+8QQOBnIlCwYEHT+i9aq8anlq1auhE2CpZoPRufKlasaAro6LpG46SlpblbXbp0cVOQ/v73v9vy5cvd9KXChQtbqVKl7LHHHnMLHDdv3txmzpzpm7JGjRq5NrTWjQI8GSVNn1LgSIsKKzikKVCXXnppRsVD17/77jurVKlS6JwMAggggAACCCCAAAIIZF+AoE32rSiJAAIIxFVAwQ2tY+PTFe2usPr161uTJk1s5MiR/rLbYergwYPuuoIy2qVp/PjxbspS//79beDAgVazZk13VKUhQ4a4kTGaIrV7927r169fqK1hw4bZxIkTrU7tOq6cAjeq6xcx1jo4gwcPduU1jUrTqbp162Z9+/Z1U7dCDUXIKJi0Y8cOF4yKcJtLCCCAAAIIIIAAAgggkIVAgZPrFJzIogy3EUAAAQTyQGDNmjV2yy23uPVktHivT5pupBE42j5b1zUiR0nXkgolWVpqmil4o2lLSvqfdU1n0ggbXVcgJiUlxUqUKGFFixZ1ZYI/dF87PJUuXdqVD96LlNfoGb8uTqT7uqaROwoEKfjz7LPPWu2T06pICCCAAAIIIIAAAgggkH0B/WMqQZvse1ESAQQQyBMBBVEUbAkmBWHCgzbB+4mYz05wJxH7TZ8QQAABBBBAAAEEEEgEAQVtmB6VCN8EfUAAAQQCAuEBG90qWbKkTZ8+PV9tm53VaJzAK5NFAAEEEEAAAQQQQACBCAKMtImAwiUEEEAAAQQQQAABBBBAAAEEEEDgTAow0uZM6vNsBBBAAAEEEEAAAQQQQAABBBBAIBOBQpnc4xYCCCCAAAIIJLCAtl//17/+ZcnJyVamTBm75557TlsPKYG7T9cQQAABBBBAAAEEshBgelQWQNxGAAEEEPh5CYwaNcqKFSvmAiCJ/OaLFy+2G2+80XWxbNmyLlgT3HUskftO3xBAAAEEEEAAAQSyFmB6VNZGlEAAAQQQ+JkJvPnmm7Zo0aKEf+uxY8e6Pk6bNs3WrVvntoNP+E7TQQQQQAABBBBAAIEcCTA9KkdcFEYAAQQQ+KkKvPPOO7ZlyxbT9urffvutPf3006FXHTx4sBUvXtwmT55sJ06csFq1alnLli1t7ty5VqBAAbvuuuusXLlyrvyf//xn03bnlStXtu7du9tZZ50Vamffvn02Y8YMu/LKK12gRdObunbtahdccEGojDIaMfPee+/Z3r17LTU11bV9++23h6Y+paWl2fr1661FixZ2+eWXp6urE20bP3v2bNu0aZNVq1bNrr32WqtUqZIr9+GHH9qqVatcvnfv3qY+vPvuu9a+Q3tr1bKVbd++3U25atCggS1btsxURmn58uV27NgxN7qnVKlS7lpWPzZu3GhvvPGGqb99+vSxpcuWWo3qNaxdu3ZuSte8efOsQ4cOVq9ePdfUzJkz3Tv26tUr1LTeQYG0AwcOuG3ve/bs6cxVILPvQ9/LSy+9ZOeff761adMm1J5/f31nFStWDF0ngwACCCCAAAIIJKIA06MS8VuhTwgggAACeS6g9WC0PoyCA0qacuSTghoKvjRt2tTdV0BDf+FfsmSJK3LOOee4IIuCE7p35MgRX9VuvfVW+93vfufOFcTo2LGjCySsXbs2VOaxxx6zgQMHunMFi0aPHu3yChRpu3cFkRS8UCBC6eDBg9a4cWP75S9/aY888oi75n8cTz1uPXv0tGD7epd//vOfLtik9p944gnXx+HDh9ukSZN8VXvqqafc1LChQ4da1apVbffu3ekcZDP2f8bagP4DQnUyyig4omCRkt7Dt9etWzcbP368LV261Pr162dPPvmkC3qpnII5JUqUcN+DzhVI0zsGkwJh6rOCZ5l9H/rOLrnkEvdsTSXzSQG4hQsXumCVpsGREEAAAQQQQACBRBVgelSifjP0CwEEEEAgzwUef/xxN3pFARiNztBIFv85++yzXcBE5woaKPiiIM5HH31kCnBodMqXX35pBQsWtM2bN9uOHTtcwKF169Y2ceJEdx58IY2gUcBHI2oU0Hj11VdDt6dOneryCmqoLZXRUQEbBRs6d+4cCoa8/PLL7lzXnn32WVdv3qvzXMCmR48ebjTPH/7wBxdo0gggpVtuucWNflH+xRdfNE2v0nsorVixwh31495777Xrr7/eBVGCgZTNmzaHymSW8c/TaBe136VLl3TBrMzq+nsPPfSQCxopeKOg1S9+8Qt7/fXXnb88Mvs+vv76a7vhhhts69atpgCSkkYKybBTp04uOOWfwxEBBBBAAAEEEEhUgYKJ2jH6hQACCCCAQCILDBo8yO3Y9Ktf/cqmTJliRYsWdd3VKBhN53nttdesRo0a7pqmXQWTgga1a9e2ChUqWKtWrdKNitG50ogRI2zChAnp7pUvX94UCNLULCWNXtG5PpqypeRH2AwZMsRNq9JRac2aNe4Y/NGoUSM3vUo7T73wwgtuCpO/rxE+CihpKpQ+fvqXRrhkJ6kfdevWtcsuu8xNeRo0aFB2qoXKHDp0yAXDNHVsz549bkqXn0alAE54ivR9KGijpMCRkg9KKYBEQgABBBBAAAEE8oMAa9rkh2+JPiKAAAIIJJxA0yZNXZ+0Zow+Slqj5bbbbnN5BSy++eYblz9+/Lg7+h/Vq1f3WUtKSgrllXn44Ydde1oLZty4ce5zzTXXuBE7CmDoo2lKGiGjgMjIkSPT1VfQSEkBHaXChQu7vJ/25S7+8CO4lo6mJinNnz//h7vRHTTSpWHDhqFGvFHoQoSM1gLSqB6llJQUd9Q0J32C6ejRo8FTl4/0fWhaWPv27d33omlkCxYscGU1RY2EAAIIIIAAAgjkBwFG2uSHb4k+IoAAAgjkmYBGmGhxXi3mm1nywYVgmTFjxpimV2lKjtZRGTN2TPB2tvIKNPz2t7919VevXu0W6tWonS2fph+tk1FjPiDkpwR99dVXbm0aPxInWE/TvuKV5OBH/egZGjkTTBrFo6SpYkpapHnXrl0urx9VqlRxeQW/5Llz587Qp2/fvqFyPhPp+9A9rRWkNYZk+Pbbb7tRScH1inx9jggggAACCCCAQCIKMNImEb8V+oQAAgggcMYEmjRpYhs2bHBrv1x11VVuJyhNZ9KIGK1Do/VqlObMmeN2ZGrbtm2orwpUaJ2V+W/Ot7TUNNM6OUqaLqXdpPwUqlCFCBktFKzAkYIQycnJoWlNhZKy93/ZWstGi/vef//97j0UqFDSrktKCib5NWwUVNF7aJSNpmrFMslM08aGDRvm1pDRWjTBpEWEFbhRGQXItNaMkqZCabTP1VdfbZp69swzz7i1bDSlSaOHtLuXFhPWGkJZfR9q74orr3DvJhMFhW6++WZdJiGAAAIIIIAAAvlCIHu/AeaLV6GTCCCAAAIIRC/wm9/8xk1reuutt0wfJe0UpUDKnXfeGXqA8lp/Jhi0uWvEXfbA/Q/YrcNvdQGJ3//+9/bggw+atrLWmjBa2Der5HeO8uU0KmTUqFFuDRx/ze8i5Y/+uo5a90W7M+nZWgRZ6aabbnI7NSn/xz/+MTQCRoEUfWbNmhUK2mgxZSUdI7WvawqybNu2zZUL/6HpWFqvRz5asFk7cumjKV7BpHIjHxppE56cYBqh5G1Wrlzp1vJR0Oa+++6zQoUKmRZn1qgjnwbcOMA+/vjjLL8PlVewa8CAAa5NnWuLdRICCCCAAAIIIJBfBNjyO798U/QTAQQQQCBPBbQ2jD4K1minqJwkjQbR4r4KfGhakEbYKEgRKQgS3q4CIloPRuvgqA19slMvvB0tGKypUeq7nh3LpIBNMFgVbFsjdrTjlU96f41S0sghbVupHaAUVPJJ26RrS/PSpUvb4cOHnVmRIkXc0ZfRUSZa80YBtJy+j0ZHadSUdtnStuYkBBBAAAEEEEAgPwjodydG2uSHb4o+IoAAAgjkuYAPmOTmwX6nJdVVMCInSQEJv4hwTuqFl1Wgp1KlSuGXY3KudXPCpzv5hsMDKlm9vwJbvkxG69Ko7ZxO31LwS+v6LF++PLR71K9//WvfTY4IIIAAAggggEC+ECBoky++JjqJAAIIIIBA4ggoMBPceSpxevZjT7RzV58+fdwFbb0+ffp0O++8834sQA4BBBBAAAEEEMgHAkyPygdfEl1EAAEEEEDgpyCgXbm0Rk+8RgAFjTTSZtWqVaaATaSds4JlySOAAAIIIIAAAokooOlRBG0S8ZuhTwgggAACCCCAAAIIIIAAAggg8LMWUNDm1BYRP2sGXh4BBBBAAIH8I6AtsZctW5ZwHU5JSXH90sLDJAQQQAABBBBAAIHYCBC0iY0jrSCAAAIIIBBXAU0tateunbVo0SLdVtfxeOiaNWvcFtn79u3LdvPaMatv377WqFEjGzx4sNs1K9uVKYgAAggggAACCCAQUYCgTUQWLiKAAAIIIJA4AsnJydatWzfbunWrjRo1ymbOnBnq3NixY61+/fr25JNPhq7dfffdbmvt0IUcZlauXGnjxo2zL774Its1taPUvHnzbNiwYbZw4ULr3bu3aTtvEgIIIIAAAggggEDuBQja5N6OmggggAACCOSJwKRJk+zIkSM2Y8YMGzJkiNWuXTv03MOHD7t7U6dONS2+q6QpSgcOHAiVyYuMthi/8MIL7cEHH7Q77rjDNm7caPPnz8+LR/MMBBBAAAEEEEDgJyvAlt8/2a+WF0MAAQQQ+KkIKFjTrFkza9OmTYav9PXXX9vbb79tXbt2jVjmnXfesffff9+KFy9unTp1ssaNG4fKaUTMyy+/bOvWrbOmTZtaampq6F4ws3fvXnvllVdsx44dLnCkLbVLlSoVLOLyQ4cOddOrpk2blmF/TqvEBQQQQAABBBBAAIHTBAjanEbCBQQQQAABBBJHQMEYjbK59NJLM+yUttFu0qSJPf/88xGDJJpS9de//jVU/4knnrApU6bY1Vdf7a6NGDHC5syZE7p/zjnnhPI+oylaCvYER/CozQULFliJEiV8MXdUfxRk2rVrV7rrnCCAAAIIIIAAAgjkTIDpUTnzojQCCCCAAAJ5KvD555+751WpUiXT5w4aNMiWLl1q27ZtS1dOgRMFV+rVq+fuz5o1y91/+OGH3VHr5Chg06pVKzfSRuvZfPfdd+na0MmECRNcwGby5MlubZ1HH33UBWWmT59+WlldqFq1qisX8SYXEUAAAQQQQAABBLIlQNAmW0wUQgABBBBAILEFOnbsaBUqVLAXXnghXUc3bNjgznv06GG1atWySy65xFq3bu0CLlr75qMNH7n7vXr3snLlylm1atVCI3CCDa1evdq1X7JkSbe1txYeVtq8eXOwGHkEEEAAAQQQQACBGAowPSqGmDSFAAIIIIBArAV8cGTPnj2ZNl24cGHr37+/aUHiiy66KFT24MGDLq9gjE++TQVt/P0a1Wv421ajxo95fzElJcU0VevGG2/0l9zx6NGj6c79iXaeqlu3rj/liAACCCCAAAIIIJALAUba5AKNKggggAACCOSVQPny5d2jli9fnuUjBwwY4KYwacttn3wARiNlfFq1apXLVqpcySpXquzyH310asSNToLr1ribJ3/UqVPHZTUFa+fOnaFPcKtxX1bBID3PP9tf54gAAggggAACCCCQMwFG2uTMi9IIIIAAAgjkqYC20lYwRjsxKRDSvHnzDJ+v0TQdOnRwiwP7Qhp1o/VlVF9taUHh7du3W/fu3a1QUiG3lo12lJo4caIVLFjQkpKS3GgdX98ftdX4kiVLrG/fvqapVtp9SiNvevfu7aZV+XI6/uMf/3Cn2l2KhAACCCCAAAIIIJB7AUba5N6OmggggAACCOSJwPDhw91z+vXrZ3PnznXBEv9gBWKCSQsSB5OmTWmnKO0IpfVuNAqnbdu2ph2llEqXLu0CNlp8ePTo0fb4449b586dg024vHaOGjdunGk6lAI8t912m40cOdI2bd4UKqtFkLVg8ZgxY9zUqO7XdA/dI4MAAggggAACCCCQc4ECJ06mnFejBgIIrvt+bAAABBFJREFUIIAAAgjkpcCaNWtMo100ukXTjrIzXSq8f/v377ciRYqctkW3yunXAbWtxYyPHz9uqampVqxYsfAm3LnWwdEaN2XKlLFSpUq5axrBox2olFq0aGHPPPOMnX322e6cHwgggAACCCCAAAI5F6hZs6YRtMm5GzUQQAABBBA4IwLHU4/blk+2uK20u3btekb6kNFDFch57733rGnTpqZfMEgIIIAAAggggAAC0QkQtInOj9oIIIAAAggggAACCCCAAAIIIIBAXAQUtGFNm7jQ0igCCCCAAAIIIIAAAggggAACCCAQnQBBm+j8qI0AAggggAACCCCAAAIIIIAAAgjERYCgTVxYaRQBBBBAAAEEEEAAAQQQQAABBBCIToCgTXR+1EYAAQQQQAABBBBAAAEEEEAAAQTiIkDQJi6sNIoAAggggAACCCCAAAIIIIAAAghEJ0DQJjo/aiOAAAIIIIAAAggggAACCCCAAAJxESBoExdWGkUAAQQQQAABBBBAAAEEEEAAAQSiEyBoE50ftRFAAAEEEEAAAQQQQAABBBBAAIG4CBC0iQsrjSKAAAIIIIAAAggggAACCCCAAALRCRC0ic6P2ggggAACCCCAAAIIIIAAAggggEBcBAjaxIWVRhFAAAEEEEAAAQQQQAABBBBAAIHoBAjaROdHbQQQQAABBBBAAAEEEEAAAQQQQCAuAgRt4sJKowgggAACCCCAAAIIIIAAAggggEB0AgRtovOjNgIIIIAAAggggAACCCCAAAIIIBAXAYI2cWGlUQQQQAABBBBAAAEEEEAAAQQQQCA6AYI20flRGwEEEEAAAQQQQAABBBBAAAEEEIiLAEGbuLDSKAIIIIAAAggggAACCCCAAAIIIBCdAEGb6PyojQACCCCAAAIIIIAAAggggAACCMRFgKBNXFhpFAEEEEAAAQQQQAABBBBAAAEEEIhOgKBNdH7URgABBBBAAAEEEEAAAQQQQAABBOIiQNAmLqw0igACCCCAAAIIIIAAAggggAACCEQnQNAmOj9qI4AAAggggAACCCCAAAIIIIAAAnERIGgTF1YaRQABBBBAAAEEEEAAAQQQQAABBKITIGgTnR+1EUAAAQQQQAABBBBAAAEEEEAAgbgIELSJCyuNIoAAAggggAACCCCAAAIIIIAAAtEJELSJzo/aCCCAAAIIIIAAAggggAACCCCAQFwECNrEhZVGEUAAAQQQQAABBBBAAAEEEEAAgegECNpE50dtBBBAAAEEEEAAAQQQQAABBBBAIC4CBG3iwkqjCCCAAAIIIIAAAggggAACCCCAQHQCBG2i86M2AggggAACCCCAAAIIIIAAAgggEBcBgjZxYaVRBBBAAAEEEEAAAQQQQAABBBBAIDqB/wf0C6P5csPYxwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodes and Edges\n",
    "\n",
    "Each `node` will simply modify the `state`.\n",
    "\n",
    "Each `edge` will choose which `node` to call next.\n",
    "\n",
    "We can lay out `self-RAG` as a graph.\n",
    "\n",
    "Here is our graph flow:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { z } from \"zod\";\n",
    "import { ChatPromptTemplate } from \"@langchain/core/prompts\";\n",
    "import { pull } from \"langchain/hub\";\n",
    "import { ChatOpenAI } from \"@langchain/openai\";\n",
    "import { StringOutputParser } from \"@langchain/core/output_parsers\";\n",
    "import type { RunnableConfig } from \"@langchain/core/runnables\";\n",
    "import { formatDocumentsAsString } from \"langchain/util/document\";\n",
    "\n",
    "// Define the LLM once. We'll reuse it throughout the graph.\n",
    "const model = new ChatOpenAI({\n",
    "  model: \"gpt-4o\",\n",
    "  temperature: 0,\n",
    "});\n",
    "\n",
    "/**\n",
    " * Retrieve documents\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function retrieve(\n",
    "  state: typeof GraphState.State,\n",
    "  config?: RunnableConfig\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---RETRIEVE---\");\n",
    "\n",
    "  const documents = await retriever\n",
    "    .withConfig({ runName: \"FetchRelevantDocuments\" })\n",
    "    .invoke(state.question, config);\n",
    "\n",
    "  return {\n",
    "    documents,\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Generate answer\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function generate(\n",
    "  state: typeof GraphState.State\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---GENERATE---\");\n",
    "\n",
    "  // Pull in the prompt\n",
    "  const prompt = await pull<ChatPromptTemplate>(\"rlm/rag-prompt\");\n",
    "  // Construct the RAG chain by piping the prompt, model, and output parser\n",
    "  const ragChain = prompt.pipe(model).pipe(new StringOutputParser());\n",
    "\n",
    "  const generation = await ragChain.invoke({\n",
    "    context: formatDocumentsAsString(state.documents),\n",
    "    question: state.question,\n",
    "  });\n",
    "\n",
    "  return {\n",
    "    generation,\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Determines whether the retrieved documents are relevant to the question.\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function gradeDocuments(\n",
    "  state: typeof GraphState.State\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---CHECK RELEVANCE---\");\n",
    "\n",
    "  // pass the name & schema to `withStructuredOutput` which will force the model to call this tool.\n",
    "  const llmWithTool = model.withStructuredOutput(\n",
    "    z\n",
    "      .object({\n",
    "        binaryScore: z\n",
    "          .enum([\"yes\", \"no\"])\n",
    "          .describe(\"Relevance score 'yes' or 'no'\"),\n",
    "      })\n",
    "      .describe(\n",
    "        \"Grade the relevance of the retrieved documents to the question. Either 'yes' or 'no'.\"\n",
    "      ),\n",
    "    {\n",
    "      name: \"grade\",\n",
    "    }\n",
    "  );\n",
    "\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `You are a grader assessing relevance of a retrieved document to a user question.\n",
    "  Here is the retrieved document:\n",
    "  \n",
    "  {context}\n",
    "  \n",
    "  Here is the user question: {question}\n",
    "\n",
    "  If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant.\n",
    "  Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.`\n",
    "  );\n",
    "\n",
    "  // Chain\n",
    "  const chain = prompt.pipe(llmWithTool);\n",
    "\n",
    "  const filteredDocs: Array<DocumentInterface> = [];\n",
    "  for await (const doc of state.documents) {\n",
    "    const grade = await chain.invoke({\n",
    "      context: doc.pageContent,\n",
    "      question: state.question,\n",
    "    });\n",
    "    if (grade.binaryScore === \"yes\") {\n",
    "      console.log(\"---GRADE: DOCUMENT RELEVANT---\");\n",
    "      filteredDocs.push(doc);\n",
    "    } else {\n",
    "      console.log(\"---GRADE: DOCUMENT NOT RELEVANT---\");\n",
    "    }\n",
    "  }\n",
    "\n",
    "  return {\n",
    "    documents: filteredDocs,\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Transform the query to produce a better question.\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function transformQuery(\n",
    "  state: typeof GraphState.State\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---TRANSFORM QUERY---\");\n",
    "\n",
    "  // Pull in the prompt\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `You are generating a question that is well optimized for semantic search retrieval.\n",
    "  Look at the input and try to reason about the underlying sematic intent / meaning.\n",
    "  Here is the initial question:\n",
    "  \\n ------- \\n\n",
    "  {question} \n",
    "  \\n ------- \\n\n",
    "  Formulate an improved question: `\n",
    "  );\n",
    "\n",
    "  // Construct the chain\n",
    "  const chain = prompt.pipe(model).pipe(new StringOutputParser());\n",
    "  const betterQuestion = await chain.invoke({ question: state.question });\n",
    "\n",
    "  return {\n",
    "    question: betterQuestion,\n",
    "  };\n",
    "}\n",
    "\n",
    "/**\n",
    " * Determines whether to generate an answer, or re-generate a question.\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @returns {\"transformQuery\" | \"generate\"} Next node to call\n",
    " */\n",
    "function decideToGenerate(state: typeof GraphState.State) {\n",
    "  console.log(\"---DECIDE TO GENERATE---\");\n",
    "\n",
    "  const filteredDocs = state.documents;\n",
    "  if (filteredDocs.length === 0) {\n",
    "    // All documents have been filtered checkRelevance\n",
    "    // We will re-generate a new query\n",
    "    console.log(\"---DECISION: TRANSFORM QUERY---\");\n",
    "    return \"transformQuery\";\n",
    "  }\n",
    "\n",
    "  // We have relevant documents, so generate answer\n",
    "  console.log(\"---DECISION: GENERATE---\");\n",
    "  return \"generate\";\n",
    "}\n",
    "\n",
    "/**\n",
    " * Determines whether the generation is grounded in the document.\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function generateGenerationVDocumentsGrade(\n",
    "  state: typeof GraphState.State\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---GENERATE GENERATION vs DOCUMENTS GRADE---\");\n",
    "\n",
    "  const llmWithTool = model.withStructuredOutput(\n",
    "    z\n",
    "      .object({\n",
    "        binaryScore: z\n",
    "          .enum([\"yes\", \"no\"])\n",
    "          .describe(\"Relevance score 'yes' or 'no'\"),\n",
    "      })\n",
    "      .describe(\n",
    "        \"Grade the relevance of the retrieved documents to the question. Either 'yes' or 'no'.\"\n",
    "      ),\n",
    "    {\n",
    "      name: \"grade\",\n",
    "    }\n",
    "  );\n",
    "\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `You are a grader assessing whether an answer is grounded in / supported by a set of facts.\n",
    "  Here are the facts:\n",
    "  \\n ------- \\n\n",
    "  {documents} \n",
    "  \\n ------- \\n\n",
    "  Here is the answer: {generation}\n",
    "  Give a binary score 'yes' or 'no' to indicate whether the answer is grounded in / supported by a set of facts.`\n",
    "  );\n",
    "\n",
    "  const chain = prompt.pipe(llmWithTool);\n",
    "\n",
    "  const score = await chain.invoke({\n",
    "    documents: formatDocumentsAsString(state.documents),\n",
    "    generation: state.generation,\n",
    "  });\n",
    "\n",
    "  return {\n",
    "    generationVDocumentsGrade: score.binaryScore,\n",
    "  };\n",
    "}\n",
    "\n",
    "function gradeGenerationVDocuments(state: typeof GraphState.State) {\n",
    "  console.log(\"---GRADE GENERATION vs DOCUMENTS---\");\n",
    "\n",
    "  const grade = state.generationVDocumentsGrade;\n",
    "  if (grade === \"yes\") {\n",
    "    console.log(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\");\n",
    "    return \"supported\";\n",
    "  }\n",
    "\n",
    "  console.log(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\");\n",
    "  return \"not supported\";\n",
    "}\n",
    "\n",
    "/**\n",
    " * Determines whether the generation addresses the question.\n",
    " *\n",
    " * @param {typeof GraphState.State} state The current state of the graph.\n",
    " * @param {RunnableConfig | undefined} config The configuration object for tracing.\n",
    " * @returns {Promise<Partial<typeof GraphState.State>>} The new state object.\n",
    " */\n",
    "async function generateGenerationVQuestionGrade(\n",
    "  state: typeof GraphState.State\n",
    "): Promise<Partial<typeof GraphState.State>> {\n",
    "  console.log(\"---GENERATE GENERATION vs QUESTION GRADE---\");\n",
    "\n",
    "  const llmWithTool = model.withStructuredOutput(\n",
    "    z\n",
    "      .object({\n",
    "        binaryScore: z\n",
    "          .enum([\"yes\", \"no\"])\n",
    "          .describe(\"Relevance score 'yes' or 'no'\"),\n",
    "      })\n",
    "      .describe(\n",
    "        \"Grade the relevance of the retrieved documents to the question. Either 'yes' or 'no'.\"\n",
    "      ),\n",
    "    {\n",
    "      name: \"grade\",\n",
    "    }\n",
    "  );\n",
    "\n",
    "  const prompt = ChatPromptTemplate.fromTemplate(\n",
    "    `You are a grader assessing whether an answer is useful to resolve a question.\n",
    "  Here is the answer:\n",
    "  \\n ------- \\n\n",
    "  {generation} \n",
    "  \\n ------- \\n\n",
    "  Here is the question: {question}\n",
    "  Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question.`\n",
    "  );\n",
    "\n",
    "  const chain = prompt.pipe(llmWithTool);\n",
    "\n",
    "  const score = await chain.invoke({\n",
    "    question: state.question,\n",
    "    generation: state.generation,\n",
    "  });\n",
    "\n",
    "  return {\n",
    "    generationVQuestionGrade: score.binaryScore,\n",
    "  };\n",
    "}\n",
    "\n",
    "function gradeGenerationVQuestion(state: typeof GraphState.State) {\n",
    "  console.log(\"---GRADE GENERATION vs QUESTION---\");\n",
    "\n",
    "  const grade = state.generationVQuestionGrade;\n",
    "  if (grade === \"yes\") {\n",
    "    console.log(\"---DECISION: USEFUL---\");\n",
    "    return \"useful\";\n",
    "  }\n",
    "\n",
    "  console.log(\"---DECISION: NOT USEFUL---\");\n",
    "  return \"not useful\";\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Graph\n",
    "\n",
    "The just follows the flow we outlined in the figure above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import { END, START, StateGraph } from \"@langchain/langgraph\";\n",
    "\n",
    "const workflow = new StateGraph(GraphState)\n",
    "  // Define the nodes\n",
    "  .addNode(\"retrieve\", retrieve)\n",
    "  .addNode(\"gradeDocuments\", gradeDocuments)\n",
    "  .addNode(\"generate\", generate)\n",
    "  .addNode(\n",
    "    \"generateGenerationVDocumentsGrade\",\n",
    "    generateGenerationVDocumentsGrade\n",
    "  )\n",
    "  .addNode(\"transformQuery\", transformQuery)\n",
    "  .addNode(\n",
    "    \"generateGenerationVQuestionGrade\",\n",
    "    generateGenerationVQuestionGrade\n",
    "  );\n",
    "\n",
    "// Build graph\n",
    "workflow.addEdge(START, \"retrieve\");\n",
    "workflow.addEdge(\"retrieve\", \"gradeDocuments\");\n",
    "workflow.addConditionalEdges(\"gradeDocuments\", decideToGenerate, {\n",
    "  transformQuery: \"transformQuery\",\n",
    "  generate: \"generate\",\n",
    "});\n",
    "workflow.addEdge(\"transformQuery\", \"retrieve\");\n",
    "workflow.addEdge(\"generate\", \"generateGenerationVDocumentsGrade\");\n",
    "workflow.addConditionalEdges(\n",
    "  \"generateGenerationVDocumentsGrade\",\n",
    "  gradeGenerationVDocuments,\n",
    "  {\n",
    "    supported: \"generateGenerationVQuestionGrade\",\n",
    "    \"not supported\": \"generate\",\n",
    "  }\n",
    ");\n",
    "\n",
    "workflow.addConditionalEdges(\n",
    "  \"generateGenerationVQuestionGrade\",\n",
    "  gradeGenerationVQuestion,\n",
    "  {\n",
    "    useful: END,\n",
    "    \"not useful\": \"transformQuery\",\n",
    "  }\n",
    ");\n",
    "\n",
    "// Compile\n",
    "const app = workflow.compile();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---RETRIEVE---\n",
      "Node: 'retrieve'\n",
      "Retrieved 4 documents.\n",
      "\n",
      "---ITERATION END---\n",
      "\n",
      "---CHECK RELEVANCE---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---DECIDE TO GENERATE---\n",
      "---DECISION: GENERATE---\n",
      "Node: 'gradeDocuments'\n",
      "Graded documents. Found 3 relevant document(s).\n",
      "\n",
      "---ITERATION END---\n",
      "\n",
      "---GENERATE---\n",
      "Node: 'generate'\n",
      "{\n",
      "  generation: 'Short-term memory in agents involves in-context learning, which is limited by the finite context window length of the model. Long-term memory allows the agent to retain and recall extensive information over extended periods by using an external vector store and fast retrieval mechanisms. Sensory memory involves learning embedding representations for raw inputs like text and images.'\n",
      "}\n",
      "\n",
      "---ITERATION END---\n",
      "\n",
      "---GENERATE GENERATION vs DOCUMENTS GRADE---\n",
      "---GRADE GENERATION vs DOCUMENTS---\n",
      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
      "Node: 'generateGenerationVDocumentsGrade'\n",
      "{ generationVDocumentsGrade: 'yes' }\n",
      "\n",
      "---ITERATION END---\n",
      "\n",
      "---GENERATE GENERATION vs QUESTION GRADE---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: USEFUL---\n",
      "Node: 'generateGenerationVQuestionGrade'\n",
      "{ generationVQuestionGrade: 'yes' }\n",
      "\n",
      "---ITERATION END---\n",
      "\n"
     ]
    }
   ],
   "source": [
    "const inputs = {\n",
    "  question: \"Explain how the different types of agent memory work.\",\n",
    "};\n",
    "const config = { recursionLimit: 50 };\n",
    "\n",
    "const prettifyOutput = (output: Record<string, any>) => {\n",
    "  const key = Object.keys(output)[0];\n",
    "  const value = output[key];\n",
    "  console.log(`Node: '${key}'`);\n",
    "  if (key === \"retrieve\" && \"documents\" in value) {\n",
    "    console.log(`Retrieved ${value.documents.length} documents.`);\n",
    "  } else if (key === \"gradeDocuments\" && \"documents\" in value) {\n",
    "    console.log(`Graded documents. Found ${value.documents.length} relevant document(s).`);\n",
    "  } else {\n",
    "    console.dir(value, { depth: null });\n",
    "  }\n",
    "}\n",
    "\n",
    "for await (const output of await app.stream(inputs, config)) {\n",
    "  prettifyOutput(output);\n",
    "  console.log(\"\\n---ITERATION END---\\n\");\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #### See the LangSmith trace [here](https://smith.langchain.com/public/cbf3c09a-5104-45f4-bd32-6e992e67f67a/r)."
   ]
  }
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